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Available online ,
doi: 10.11999/JEIT211164
Abstract:
With the progress of modern communication technology, especially the rapid development of 4G, 5G and other wireless mobile communications, modulation methods with high spectrum efficiency such as multi-Quadrature Amplitude Modulation (QAM) have been widely used, which puts forward higher and stricter linear requirements for wireless communication systems. The Radio Frequency Low Noise Amplifier(RF LNA)is the first active device of the RF Front-End Module(RF FEM), and the signal quality and dynamic range of the system are directly affected by the the nonlinear characteristics of the LNA. Taking the 3rd-order intermodulation as an example, a sufficient input 3rd-order intercept point is required in LNA to ensure expected performance even with strong interfering signals. Based on the 3rd-order nonlinear model, in this article, the theoretical model of the 3rd-order intermodulation is analyzed briefly, the methods to improve the 3rd-order intercept point are sorted out, the relevant research results and progress in recent years are summarized and studied , and the future development trend is prospected.
With the progress of modern communication technology, especially the rapid development of 4G, 5G and other wireless mobile communications, modulation methods with high spectrum efficiency such as multi-Quadrature Amplitude Modulation (QAM) have been widely used, which puts forward higher and stricter linear requirements for wireless communication systems. The Radio Frequency Low Noise Amplifier(RF LNA)is the first active device of the RF Front-End Module(RF FEM), and the signal quality and dynamic range of the system are directly affected by the the nonlinear characteristics of the LNA. Taking the 3rd-order intermodulation as an example, a sufficient input 3rd-order intercept point is required in LNA to ensure expected performance even with strong interfering signals. Based on the 3rd-order nonlinear model, in this article, the theoretical model of the 3rd-order intermodulation is analyzed briefly, the methods to improve the 3rd-order intercept point are sorted out, the relevant research results and progress in recent years are summarized and studied , and the future development trend is prospected.
Available online ,
doi: 10.11999/JEIT220240
Abstract:
With the rapid development of Internet of Things (IoT), the deep integration of Artificial Intelligence (AI) and Edge Computing (EC) has formed Edge AI. However, since IoT devices are computationally and communicationally constrained and these devices often require privacy-preserving, it is still a challenge to accelerate Edge AI while protecting privacy. Federated learning (FL), an emerging distributed learning paradigm, has great potential in terms of privacy preservation and improving model performance, but communication and local training are inefficient. To address the above challenges, a FL acceleration framework AccFed is proposed in this paper. Firstly, a Device-Edge-Cloud synergy training algorithm based on model partitioning is proposed to accelerate FL local training according to the different network states; Then, a multi-iteration and reaggregation algorithm is designed to accelerate FL aggregation; Finally, experimental results show that AccFed outperforms the control group in terms of training accuracy, convergence speed, training time, etc.
With the rapid development of Internet of Things (IoT), the deep integration of Artificial Intelligence (AI) and Edge Computing (EC) has formed Edge AI. However, since IoT devices are computationally and communicationally constrained and these devices often require privacy-preserving, it is still a challenge to accelerate Edge AI while protecting privacy. Federated learning (FL), an emerging distributed learning paradigm, has great potential in terms of privacy preservation and improving model performance, but communication and local training are inefficient. To address the above challenges, a FL acceleration framework AccFed is proposed in this paper. Firstly, a Device-Edge-Cloud synergy training algorithm based on model partitioning is proposed to accelerate FL local training according to the different network states; Then, a multi-iteration and reaggregation algorithm is designed to accelerate FL aggregation; Finally, experimental results show that AccFed outperforms the control group in terms of training accuracy, convergence speed, training time, etc.
Available online ,
doi: 10.11999/JEIT211426
Abstract:
A great improvement in Underwater Acoustic Network (UAN) has been witnessed in past few years, but severe challenges still remain, and energy efficiency becomes the primary consideration of UAN. In addition, the reliability and effectiveness of underwater acoustic communication technology are seriously restricted by the large propagation delay of the underwater acoustic channel and the limitation of available bandwidth, and the performance of UAN is limited. Through the ability to focus a beam, the above challenges can be effectively addressed by directional communication technology, resulting in a higher communication range and signal-to-noise ratio than omnidirectional communication, as well as energy consumption efficiency and spatial reuse ratio of the whole network are improved. However, a priori knowledge of the location of the destination node is required and the problem of deafness occurs. Therefore, the Full-Duplex Directional Collision Avoidance (FDDCA) Medium Access Control (MAC) protocol is proposed in this paper, with which the problem of deafness is resolved by using two transducers that work in omnidirectional and directional modes, respectively, as well as the exposed terminal problem. Results supporting the conclusions are shown in the simulations, where 90% and 94% energy savings, 140% and 400% throughput improvements are acquired in different network topologies by FDDCA, compared with UnderWater Aloha (UW-Aloha) and Slotted FAMA (S-FAMA) protocol.
A great improvement in Underwater Acoustic Network (UAN) has been witnessed in past few years, but severe challenges still remain, and energy efficiency becomes the primary consideration of UAN. In addition, the reliability and effectiveness of underwater acoustic communication technology are seriously restricted by the large propagation delay of the underwater acoustic channel and the limitation of available bandwidth, and the performance of UAN is limited. Through the ability to focus a beam, the above challenges can be effectively addressed by directional communication technology, resulting in a higher communication range and signal-to-noise ratio than omnidirectional communication, as well as energy consumption efficiency and spatial reuse ratio of the whole network are improved. However, a priori knowledge of the location of the destination node is required and the problem of deafness occurs. Therefore, the Full-Duplex Directional Collision Avoidance (FDDCA) Medium Access Control (MAC) protocol is proposed in this paper, with which the problem of deafness is resolved by using two transducers that work in omnidirectional and directional modes, respectively, as well as the exposed terminal problem. Results supporting the conclusions are shown in the simulations, where 90% and 94% energy savings, 140% and 400% throughput improvements are acquired in different network topologies by FDDCA, compared with UnderWater Aloha (UW-Aloha) and Slotted FAMA (S-FAMA) protocol.
Available online ,
doi: 10.11999/JEIT211499
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For co-frequency and co-time full duplex frequency hopping ad hoc network in finite area, communication performance analysis is proposed considering the scenario of self-interference and asymmetric mutual interference caused by unequal position of communication nodes. Taking the network band utilization as the performance index, the closed expression of network band utilization under the condition of node location distribution is derived, and a node location optimization distribution method reducing network mutual interference is proposed. Simulation results show that the performance of full duplex frequency hopping ad hoc network in finite area is strongly related to the number of frequency points, communication distance and the number of nodes. Besides, whether the performance of full duplex ad hoc network is better than half duplex network depends on the number of nodes.
For co-frequency and co-time full duplex frequency hopping ad hoc network in finite area, communication performance analysis is proposed considering the scenario of self-interference and asymmetric mutual interference caused by unequal position of communication nodes. Taking the network band utilization as the performance index, the closed expression of network band utilization under the condition of node location distribution is derived, and a node location optimization distribution method reducing network mutual interference is proposed. Simulation results show that the performance of full duplex frequency hopping ad hoc network in finite area is strongly related to the number of frequency points, communication distance and the number of nodes. Besides, whether the performance of full duplex ad hoc network is better than half duplex network depends on the number of nodes.
Available online ,
doi: 10.11999/JEIT210743
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In order to solve the problem of virtual network function migration caused by time-varying network traffic in network slicing, a Virtual Network Function (VNF) migration algorithm based on Federated learning with Bidirectional Gate Recurrent Units (FedBi-GRU) prediction of resource requirements is proposed. Firstly, a VNF migration model of system energy consumption and load balancing is established, and then a framework based on distributed federated learning is introduced to cooperatively train the predictive model. Secondly, Considering predicting the resource requirements of VNF, an online training Bidirectional Gate Recurrent Unit (Bi-GRU) algorithm on the basis of the framework is designed. Finally, on the grounds of the resource prediction results, system energy consumption optimization and load balancing are combined, and a Distributed Proximal Policy Optimization (DPPO) migration algorithm is proposed to formulate a VNF migration strategy in advance. The simulation results show that the combination of the two algorithms effectively reduces the energy consumption of the network system and ensure the load balance.
In order to solve the problem of virtual network function migration caused by time-varying network traffic in network slicing, a Virtual Network Function (VNF) migration algorithm based on Federated learning with Bidirectional Gate Recurrent Units (FedBi-GRU) prediction of resource requirements is proposed. Firstly, a VNF migration model of system energy consumption and load balancing is established, and then a framework based on distributed federated learning is introduced to cooperatively train the predictive model. Secondly, Considering predicting the resource requirements of VNF, an online training Bidirectional Gate Recurrent Unit (Bi-GRU) algorithm on the basis of the framework is designed. Finally, on the grounds of the resource prediction results, system energy consumption optimization and load balancing are combined, and a Distributed Proximal Policy Optimization (DPPO) migration algorithm is proposed to formulate a VNF migration strategy in advance. The simulation results show that the combination of the two algorithms effectively reduces the energy consumption of the network system and ensure the load balance.
Available online ,
doi: 10.11999/JEIT211188
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Multi-exposure image fusion aims to fuse a series of images with different exposures for the same scene, and it is the main-stream method for high dynamic range imaging. To obtain more realistic results, a deep guided and self-learning-based network is proposed for multi-exposure image fusion. This network is designed to fuse any number of images with different exposures in an end-to-end way, and generate the best-fused results in an unsupervised way. In terms of the loss function, an intensity fidelity constraint term and the weighted structural similarity are introduced to improve the fusion quality. Moreover, a self-learning method is adopted to fine-tune and optimize the pre-learned model, aiming at the fusion problem of two images under extreme exposure to mitigate the halo phenomenon generated by fusion. Based on abundant testing data, experimental results show that the proposed algorithm outperforms other mainstream methods in terms of both quantitative measurement and visual fused quality.
Multi-exposure image fusion aims to fuse a series of images with different exposures for the same scene, and it is the main-stream method for high dynamic range imaging. To obtain more realistic results, a deep guided and self-learning-based network is proposed for multi-exposure image fusion. This network is designed to fuse any number of images with different exposures in an end-to-end way, and generate the best-fused results in an unsupervised way. In terms of the loss function, an intensity fidelity constraint term and the weighted structural similarity are introduced to improve the fusion quality. Moreover, a self-learning method is adopted to fine-tune and optimize the pre-learned model, aiming at the fusion problem of two images under extreme exposure to mitigate the halo phenomenon generated by fusion. Based on abundant testing data, experimental results show that the proposed algorithm outperforms other mainstream methods in terms of both quantitative measurement and visual fused quality.
Available online ,
doi: 10.11999/JEIT211249
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In this paper, an Integrate-and-Fire (IF) analog readout neuron circuit is proposed for Spiking Convolutional Neural Network (SCNN) based on flash array. The circuit realizes the following functions: bit line voltage clamping, current readout, current subtraction, and integrate-and-fire. A current readout method is proposed to improve the current readout range and speed by increasing by-pass current. To avoid the loss of array information caused by the traditional analog neuron reset scheme, a reset scheme with subtracting threshold voltage is proposed, which improves the integrity of information and the accuracy of the neural network. The circuit is implemented in 55 nm Complementary Metal Oxide Semiconductor (CMOS) process. Simulation results show that when output current is 20 μA and 0 μA, the read speed can be accelerated 100% and 263.6% respectively; The neuron circuit works well. And test results show that, in the current output range of 0～20 μA, the clamp voltage error is less than 0.2 mV and the fluctuation is less than 0.4 mV; The linearity of current subtraction can reach 99.9%. To study the performance of the analog neuron circuit, LeNet and AlexNet algorithm with circuit model for the recognition of the MNIST and CIFAR-10 database is tested. Test results illustrate that the neural network accuracy is improved by 1.4% and 38.8%.
In this paper, an Integrate-and-Fire (IF) analog readout neuron circuit is proposed for Spiking Convolutional Neural Network (SCNN) based on flash array. The circuit realizes the following functions: bit line voltage clamping, current readout, current subtraction, and integrate-and-fire. A current readout method is proposed to improve the current readout range and speed by increasing by-pass current. To avoid the loss of array information caused by the traditional analog neuron reset scheme, a reset scheme with subtracting threshold voltage is proposed, which improves the integrity of information and the accuracy of the neural network. The circuit is implemented in 55 nm Complementary Metal Oxide Semiconductor (CMOS) process. Simulation results show that when output current is 20 μA and 0 μA, the read speed can be accelerated 100% and 263.6% respectively; The neuron circuit works well. And test results show that, in the current output range of 0～20 μA, the clamp voltage error is less than 0.2 mV and the fluctuation is less than 0.4 mV; The linearity of current subtraction can reach 99.9%. To study the performance of the analog neuron circuit, LeNet and AlexNet algorithm with circuit model for the recognition of the MNIST and CIFAR-10 database is tested. Test results illustrate that the neural network accuracy is improved by 1.4% and 38.8%.
Available online ,
doi: 10.11999/JEIT220142
Abstract:
The robust beamforming design for the aggregated visible Light Communication (VLC) and Radio Frequency (RF) system are studied for the first time. Specifically, with imperfect Channel State Informations (CSIs) of both VLC and RF channels, robust beam formers design schemes are proposed to minimize the transmit power of the aggregated VLC-RF system, while satisfying both the minimum rate requirements and dimming control constraints. However, there are infinite constraints of the robust beamforming design problem, which is intractable in general. Through Semi-Definite Relaxation (SDR), the non-convex original problem is relaxed firstly, and then conservative reformulated it into a convex Semi-Definite Program (SDP) by exploiting\begin{document}$\mathcal{S}$\end{document} ![]()
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The robust beamforming design for the aggregated visible Light Communication (VLC) and Radio Frequency (RF) system are studied for the first time. Specifically, with imperfect Channel State Informations (CSIs) of both VLC and RF channels, robust beam formers design schemes are proposed to minimize the transmit power of the aggregated VLC-RF system, while satisfying both the minimum rate requirements and dimming control constraints. However, there are infinite constraints of the robust beamforming design problem, which is intractable in general. Through Semi-Definite Relaxation (SDR), the non-convex original problem is relaxed firstly, and then conservative reformulated it into a convex Semi-Definite Program (SDP) by exploiting
Available online ,
doi: 10.11999/JEIT220088
Abstract:
In industrial wireless sensor networks, timely delivery of periodic control/sensing data flows and aperiodic event data flows is crucial to ensure production safety and efficiency. As a new metric of data freshness, Age of Information (AoI) can comprehensively measure the real-time performance of data delivery from the perspective of destination node. For industrial wireless sensor networks with hybrid periodic and aperiodic data, the data freshness metric of whole network is introduced. Considering that the freshness of periodic data exceeding the threshold may have a negative impact on industrial production, a joint optimization model is established, which minimizes the system average AoI and the probability of AoI overdue for periodic data, and then the optimization problem is formulated as a Markov Decision Process. Since the traditional optimal solution method based on relative value iteration is difficult to implement in large-scale networks result from dimensional disasters, deep reinforcement learning is used to reduce the state space dimension of the optimization problem. Moreover, the decision exploration mechanism is improved to speed up the learning speed, and a scheduling method of deep reinforcement learning based on optimal decision exploration is proposed. Simulation results show that the proposed method can improve the timeliness of network data delivery while reducing the probability of AoI overdue for periodic data effectively.
In industrial wireless sensor networks, timely delivery of periodic control/sensing data flows and aperiodic event data flows is crucial to ensure production safety and efficiency. As a new metric of data freshness, Age of Information (AoI) can comprehensively measure the real-time performance of data delivery from the perspective of destination node. For industrial wireless sensor networks with hybrid periodic and aperiodic data, the data freshness metric of whole network is introduced. Considering that the freshness of periodic data exceeding the threshold may have a negative impact on industrial production, a joint optimization model is established, which minimizes the system average AoI and the probability of AoI overdue for periodic data, and then the optimization problem is formulated as a Markov Decision Process. Since the traditional optimal solution method based on relative value iteration is difficult to implement in large-scale networks result from dimensional disasters, deep reinforcement learning is used to reduce the state space dimension of the optimization problem. Moreover, the decision exploration mechanism is improved to speed up the learning speed, and a scheduling method of deep reinforcement learning based on optimal decision exploration is proposed. Simulation results show that the proposed method can improve the timeliness of network data delivery while reducing the probability of AoI overdue for periodic data effectively.
Available online ,
doi: 10.11999/JEIT211618
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In this work, an Intelligent Reflecting Surface (IRS) aided and Artificial Noise (AN) enhanced covert wireless communications is considered to improve the covert transmission performance. Firstly, the detection performance at Willie is analyzed and a lower bound on Willie’s minimum total detection error probability is presented. On this basis, an optimization problem that maximizes the effective throughput subject to the covertness constraint and the maximum AN transmit power constraint is formulated. The optimization problem is non-convex, which is generally difficult to tackle directly. Then, an alternating iterative algorithm based on Dinkelbach method is proposed to jointly design the IRS reflection beamforming and Alice’s transmit power together with Bob’s AN transmit power. In order to reduce the computational complexity, a low-complexity algorithm is further proposed to obtain analytical expressions for the corresponding optimization variables. Our simulation results show that the proposed scheme significantly improves the covert transmission performance compared with the schemes without IRS and without AN.
In this work, an Intelligent Reflecting Surface (IRS) aided and Artificial Noise (AN) enhanced covert wireless communications is considered to improve the covert transmission performance. Firstly, the detection performance at Willie is analyzed and a lower bound on Willie’s minimum total detection error probability is presented. On this basis, an optimization problem that maximizes the effective throughput subject to the covertness constraint and the maximum AN transmit power constraint is formulated. The optimization problem is non-convex, which is generally difficult to tackle directly. Then, an alternating iterative algorithm based on Dinkelbach method is proposed to jointly design the IRS reflection beamforming and Alice’s transmit power together with Bob’s AN transmit power. In order to reduce the computational complexity, a low-complexity algorithm is further proposed to obtain analytical expressions for the corresponding optimization variables. Our simulation results show that the proposed scheme significantly improves the covert transmission performance compared with the schemes without IRS and without AN.
Available online ,
doi: 10.11999/JEIT211605
Abstract:
During the modeling and analysis of neuronal activity, several biophysical effects should be taken into consideration. Due to fluctuations in intracellular and extracellular ion concentrations within the nervous system, internal fluctuations of electromagnetic fields and the effects of transmembrane magnetic flux need to be considered in the collective electrical activity and signal propagation between neuronal clusters. In this paper, a magnetic flux variable is introduced into a hybrid neuron, and a complex time-varying electromagnetic field is induced by modulating the membrane potential. Using analytical tools such as Xppauto, Matcont and Matlab, the existence and initial value of the equilibrium point of the new model, sensitivity and two-parameter bifurcation are discussed. When the external stimulus current and electromagnetic field change, the new model can be induced to generate abundant discharge modes, such as resting state, spike discharge, periodic (or chaotic) cluster discharge, especially coexisting discharge and hidden discharge benefited from the introduction of magnetic flux variable and memristor. According to the above analysis, the neuron model based on electromagnetic induction has high nonlinearity and more sensitive parameters, which enables the encryption algorithm to have a large key space. Based on this, this paper designs an image encryption algorithm. Pixels are first diffused once and then scrambled twice to their positions. Finally, through a series of numerical experiments, it is proved that the designed encryption algorithm can encrypt images effectively and has high security. The research takes into account the electromagnetic induction effect inside and outside the nerve cells, which is helpful for a more comprehensive understanding of the information encoding and transition laws between neurons. More bifurcation parameters and high complexity also make the designed neuron model has a good application prospect in image encryption.
During the modeling and analysis of neuronal activity, several biophysical effects should be taken into consideration. Due to fluctuations in intracellular and extracellular ion concentrations within the nervous system, internal fluctuations of electromagnetic fields and the effects of transmembrane magnetic flux need to be considered in the collective electrical activity and signal propagation between neuronal clusters. In this paper, a magnetic flux variable is introduced into a hybrid neuron, and a complex time-varying electromagnetic field is induced by modulating the membrane potential. Using analytical tools such as Xppauto, Matcont and Matlab, the existence and initial value of the equilibrium point of the new model, sensitivity and two-parameter bifurcation are discussed. When the external stimulus current and electromagnetic field change, the new model can be induced to generate abundant discharge modes, such as resting state, spike discharge, periodic (or chaotic) cluster discharge, especially coexisting discharge and hidden discharge benefited from the introduction of magnetic flux variable and memristor. According to the above analysis, the neuron model based on electromagnetic induction has high nonlinearity and more sensitive parameters, which enables the encryption algorithm to have a large key space. Based on this, this paper designs an image encryption algorithm. Pixels are first diffused once and then scrambled twice to their positions. Finally, through a series of numerical experiments, it is proved that the designed encryption algorithm can encrypt images effectively and has high security. The research takes into account the electromagnetic induction effect inside and outside the nerve cells, which is helpful for a more comprehensive understanding of the information encoding and transition laws between neurons. More bifurcation parameters and high complexity also make the designed neuron model has a good application prospect in image encryption.
Available online ,
doi: 10.11999/JEIT211405
Abstract:
Considering the deficiency that traditional intention recognition methods can only deal with certain types of uncertain information, a new information processing method of fuzzy belief-rule-base is proposed, which combines the advantages of fuzzy sets and Dempster-Shafer (DS) theory. Firstly, the connection relation of premise attributes is improved in the premise part of confidence rules, and fuzzy set segmentation is designed according to the statistical distribution characteristics of data sets. Cauchy distribution is selected as membership function to avoid the problem that confidence rules could not be activated effectively, which would lead to no effective output of the system. Secondly, the confidence distribution of different categories in the identification framework is fused, and the optimization model of rule weight and feature weight is established, and the input-output relationship between feature space and category space is constructed. On this basis, the matching degree and activation degree of the unknown intention data are calculated, and the recognition decision is made using the maximum confidence principle. Through experimental verification, sensitive parameter and interpretation of result, time complexity analysis, compared with other methods, the fuzzy belief-rule-base shows high accuracy rate, and effectiveness and reliability under the condition of small samples.
Considering the deficiency that traditional intention recognition methods can only deal with certain types of uncertain information, a new information processing method of fuzzy belief-rule-base is proposed, which combines the advantages of fuzzy sets and Dempster-Shafer (DS) theory. Firstly, the connection relation of premise attributes is improved in the premise part of confidence rules, and fuzzy set segmentation is designed according to the statistical distribution characteristics of data sets. Cauchy distribution is selected as membership function to avoid the problem that confidence rules could not be activated effectively, which would lead to no effective output of the system. Secondly, the confidence distribution of different categories in the identification framework is fused, and the optimization model of rule weight and feature weight is established, and the input-output relationship between feature space and category space is constructed. On this basis, the matching degree and activation degree of the unknown intention data are calculated, and the recognition decision is made using the maximum confidence principle. Through experimental verification, sensitive parameter and interpretation of result, time complexity analysis, compared with other methods, the fuzzy belief-rule-base shows high accuracy rate, and effectiveness and reliability under the condition of small samples.
Available online ,
doi: 10.11999/JEIT211400
Abstract:
In recent years, with the upsurging development of launching, satellite, telecommunication, and networks technologies, the form of space-based networks is undergoing qualitative changes. Low-Earth-Orbit (LEO) constellation network has become a new option for many application scenarios. With the development trend towards a space-based information infrastructure, the LEO constellation is required to possess features of global coverage, constellation-terrestrial integration, diversified carrying, continuous evolution, security, and controllability, which put forward higher requirements for the network architecture of LEO constellation in the future. Therefore, in this paper, the development status of related fields of LEO constellation networks is summarized, and the tendencies of LEO constellation network development are analyzed. On this basis, an “all-in-cloud” network architecture based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) is proposed, making the network architecture programmable, decoupled, and decentralized. Moreover, the network operation management is supported by intention-driven approaches, so as to realize the capabilities of flexible carrying, continuous evolution, and automatic management of the network. Finally, we look forward to the technical direction that needs to be focused on.
In recent years, with the upsurging development of launching, satellite, telecommunication, and networks technologies, the form of space-based networks is undergoing qualitative changes. Low-Earth-Orbit (LEO) constellation network has become a new option for many application scenarios. With the development trend towards a space-based information infrastructure, the LEO constellation is required to possess features of global coverage, constellation-terrestrial integration, diversified carrying, continuous evolution, security, and controllability, which put forward higher requirements for the network architecture of LEO constellation in the future. Therefore, in this paper, the development status of related fields of LEO constellation networks is summarized, and the tendencies of LEO constellation network development are analyzed. On this basis, an “all-in-cloud” network architecture based on Software-Defined Network (SDN) and Network Function Virtualization (NFV) is proposed, making the network architecture programmable, decoupled, and decentralized. Moreover, the network operation management is supported by intention-driven approaches, so as to realize the capabilities of flexible carrying, continuous evolution, and automatic management of the network. Finally, we look forward to the technical direction that needs to be focused on.
Available online ,
doi: 10.11999/JEIT210720
Abstract:
Linear codes play an important role in data storage, information security and secret sharing. Minimal linear codes are the first choice to design secret sharing schemes, so the design of minimal linear codes is one of the important contents of current cryptosystem and coding theory. In this paper, we study the Walsh spectrum distribution of the selected Boolean functions, and two kinds of minimal linear codes are obtained by using the Walsh spectrum distribution of the functions, then the weight distribution of the codes are determined. The results show that the constructed codes are minimal linear codes that do not satisfy the Ashikhmin-Barg condition, and can be used to design secret sharing schemes with good access structure.
Linear codes play an important role in data storage, information security and secret sharing. Minimal linear codes are the first choice to design secret sharing schemes, so the design of minimal linear codes is one of the important contents of current cryptosystem and coding theory. In this paper, we study the Walsh spectrum distribution of the selected Boolean functions, and two kinds of minimal linear codes are obtained by using the Walsh spectrum distribution of the functions, then the weight distribution of the codes are determined. The results show that the constructed codes are minimal linear codes that do not satisfy the Ashikhmin-Barg condition, and can be used to design secret sharing schemes with good access structure.
Available online ,
doi: 10.11999/JEIT210161
Abstract:
Specific radar emitter identification distinguishes each radar emitter based on the extracted individual features, which is crucial for electronic countermeasures. With the rapid development of deep learning, specific radar emitter identification using deep learning architecture draws great attention recently. Despite many years of research and rich achievements, there is still lack of a comprehensive review about specific radar emitter identification at present. Therefore, a systematic review is provided in this paper from four aspects: (1) the mechanism analysis of identification; (2) the handcrafted feature-based identification methods; (3) the deep learning-based identification methods; (4) and the testing datasets. Finally, the current status and the future directions are summarized, aiming at promoting the new development of specific radar emitter identification.
Specific radar emitter identification distinguishes each radar emitter based on the extracted individual features, which is crucial for electronic countermeasures. With the rapid development of deep learning, specific radar emitter identification using deep learning architecture draws great attention recently. Despite many years of research and rich achievements, there is still lack of a comprehensive review about specific radar emitter identification at present. Therefore, a systematic review is provided in this paper from four aspects: (1) the mechanism analysis of identification; (2) the handcrafted feature-based identification methods; (3) the deep learning-based identification methods; (4) and the testing datasets. Finally, the current status and the future directions are summarized, aiming at promoting the new development of specific radar emitter identification.
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2022, 44(4): 1147-1155.
doi: 10.11999/JEIT211044
Abstract:
Human target sensing technology with Ultra-WideBand (UWB) radar studies mainly how to recognize the position, behavior and intention of the human target according to the electromagnetic scattering echoes. It is an efficient complement to the optical-based target sensing, and can be applied to many scenarios such as the scenarios without light, the scenarios with occlusion, and the non-line-of-sight scenarios. Two key human sensing technologies are presented in this paper, i.e., the spatial location-based method and the micro-Doppler-based method, and the relevant literatures about the two technologies are summarized. Finally, The future research directions of the UWB radar-based human target sensing filed are discussed in the conclusion.
Human target sensing technology with Ultra-WideBand (UWB) radar studies mainly how to recognize the position, behavior and intention of the human target according to the electromagnetic scattering echoes. It is an efficient complement to the optical-based target sensing, and can be applied to many scenarios such as the scenarios without light, the scenarios with occlusion, and the non-line-of-sight scenarios. Two key human sensing technologies are presented in this paper, i.e., the spatial location-based method and the micro-Doppler-based method, and the relevant literatures about the two technologies are summarized. Finally, The future research directions of the UWB radar-based human target sensing filed are discussed in the conclusion.
2022, 44(4): 1156-1175.
doi: 10.11999/JEIT211051
Abstract:
In applications of human action recognition, Through-Wall Radar (TWR) is a promising tool because of its outstanding advantages in aspects of concealment, detection ability and robustness against environmental restrictions. Besides, TWR can provide targets with satisfactory privacy protection. As a result, TWR is widely used in a series of areas including anti-terrorism, security monitoring and medical caring. To hackle and forecast the development process of the TWR-based human action recognition theory, the detection principle of different kinds of TWRs is first introduced in this article, and their properties are compared. Then aiming at the key technologies involved in human action recognition, such as radar imaging, feature information extraction, and action state judgement, the relevant literature published at home and abroad is classified and analyzed. Finally, the TWR-based human action recognition theory is summarized and prospected, and some potential problems and challenges in practical applications are pointed out.
In applications of human action recognition, Through-Wall Radar (TWR) is a promising tool because of its outstanding advantages in aspects of concealment, detection ability and robustness against environmental restrictions. Besides, TWR can provide targets with satisfactory privacy protection. As a result, TWR is widely used in a series of areas including anti-terrorism, security monitoring and medical caring. To hackle and forecast the development process of the TWR-based human action recognition theory, the detection principle of different kinds of TWRs is first introduced in this article, and their properties are compared. Then aiming at the key technologies involved in human action recognition, such as radar imaging, feature information extraction, and action state judgement, the relevant literature published at home and abroad is classified and analyzed. Finally, the TWR-based human action recognition theory is summarized and prospected, and some potential problems and challenges in practical applications are pointed out.
2022, 44(4): 1176-1192.
doi: 10.11999/JEIT211082
Abstract:
Ultra-Wide Band (UWB) system has the advantages of high transmission rate, low power consumption, high detection accuracy, strong penetration, high security, etc., so it has a wide range of applications to military, radar, biological detection, short-range communications, and high-precision positioning. And with the development of semiconductor technology, CMOS-based UWB radar chips have become a research hotspot. Many scholars and commercial companies have proposed UWB chips and systems with their own advantages. This paper summarizes the status and trends of key circuits and key technologies in UWB system.
Ultra-Wide Band (UWB) system has the advantages of high transmission rate, low power consumption, high detection accuracy, strong penetration, high security, etc., so it has a wide range of applications to military, radar, biological detection, short-range communications, and high-precision positioning. And with the development of semiconductor technology, CMOS-based UWB radar chips have become a research hotspot. Many scholars and commercial companies have proposed UWB chips and systems with their own advantages. This paper summarizes the status and trends of key circuits and key technologies in UWB system.
2022, 44(4): 1193-1202.
doi: 10.11999/JEIT211203
Abstract:
Ultra-wideband radar is widely used for human rescue under ruins, due to its high range resolution and high penetration capability. However, it is difficult to rely on a single radar to detect trapped persons, under complex conditions. In this paper, a human respiration detection method under low Signal-to-Noise Ratio (SNR) conditions by distributed through-wall radar is proposed. First, two ultra-wideband impulse radars use time-division multiplexing to prevent mutual interference. Secondly, the slow-time dimension cross-correlation processing is performed on the echo data of the two radars to enhance the human respiration signal. Thirdly, the automatic detection algorithm is improved based on constant false alarm ratio to make it suitable for the distributed through-wall radar and realize automatic and fast detection. Finally, the trilateration algorithm is used to locate the trapped persons. A series of experimental results show that the method proposed can realize human detection under low SNR conditions, and its detection performance is better than that of a single radar.
Ultra-wideband radar is widely used for human rescue under ruins, due to its high range resolution and high penetration capability. However, it is difficult to rely on a single radar to detect trapped persons, under complex conditions. In this paper, a human respiration detection method under low Signal-to-Noise Ratio (SNR) conditions by distributed through-wall radar is proposed. First, two ultra-wideband impulse radars use time-division multiplexing to prevent mutual interference. Secondly, the slow-time dimension cross-correlation processing is performed on the echo data of the two radars to enhance the human respiration signal. Thirdly, the automatic detection algorithm is improved based on constant false alarm ratio to make it suitable for the distributed through-wall radar and realize automatic and fast detection. Finally, the trilateration algorithm is used to locate the trapped persons. A series of experimental results show that the method proposed can realize human detection under low SNR conditions, and its detection performance is better than that of a single radar.
2022, 44(4): 1203-1211.
doi: 10.11999/JEIT211052
Abstract:
Considering the problem of target signal identification in frequency ambiguity region of Doppler through wall radar, and the path bifurcation problem of traditional Viterbi algorithm in instantaneous frequency estimation, a target location algorithm based on improved Viterbi frequency estimation technology is proposed. According to the local characteristics of radar echo, the smoothing coefficient of exponential smoothing method is dynamically adjusted, and a new penalty function based on dynamic exponential smoothing prediction is defined. The echo signal is demodulated by the estimated target frequency curve to separate multiple target components, and the target motion trajectory is synthesized by combining Doppler processing to realize real-time target positioning. The experimental results show that the path bifurcation problem in frequency ambiguity region is effectively suppressed by this method, and this method has advantages in the application scene of multi-body target tracking and location. In addition, the employed dynamic search method enhances upon traditional whole-plane search and improves the efficiency of searching the optimal paths considerably.
Considering the problem of target signal identification in frequency ambiguity region of Doppler through wall radar, and the path bifurcation problem of traditional Viterbi algorithm in instantaneous frequency estimation, a target location algorithm based on improved Viterbi frequency estimation technology is proposed. According to the local characteristics of radar echo, the smoothing coefficient of exponential smoothing method is dynamically adjusted, and a new penalty function based on dynamic exponential smoothing prediction is defined. The echo signal is demodulated by the estimated target frequency curve to separate multiple target components, and the target motion trajectory is synthesized by combining Doppler processing to realize real-time target positioning. The experimental results show that the path bifurcation problem in frequency ambiguity region is effectively suppressed by this method, and this method has advantages in the application scene of multi-body target tracking and location. In addition, the employed dynamic search method enhances upon traditional whole-plane search and improves the efficiency of searching the optimal paths considerably.
2022, 44(4): 1212-1221.
doi: 10.11999/JEIT211078
Abstract:
At present, initial model dependence is one of the most famous problem in Full-Waveform Inversion (FWI). It is very important to use the low-frequency components to build an accurate initial model. However, insufficient low-frequency information is obtained in the field dataset of Ground Penetrating Radar (GPR), which makes it difficult ensure accurate results for FWI. Therefore, an envelope-waveform inversion method based on multi-offset GPR data is proposed, which uses the envelope operator to build a large-scale background model and describe small-scale targets. Compared with the results of conventional FWI, the envelope-waveform inversion can effectively reconstruct the missing low-frequency components and improve the imaging effect of underground large-scale background structure and detail information.
At present, initial model dependence is one of the most famous problem in Full-Waveform Inversion (FWI). It is very important to use the low-frequency components to build an accurate initial model. However, insufficient low-frequency information is obtained in the field dataset of Ground Penetrating Radar (GPR), which makes it difficult ensure accurate results for FWI. Therefore, an envelope-waveform inversion method based on multi-offset GPR data is proposed, which uses the envelope operator to build a large-scale background model and describe small-scale targets. Compared with the results of conventional FWI, the envelope-waveform inversion can effectively reconstruct the missing low-frequency components and improve the imaging effect of underground large-scale background structure and detail information.
2022, 44(4): 1222-1230.
doi: 10.11999/JEIT211142
Abstract:
The subsurface structure and composition of moon are always heterogeneous, also, both geometric shape of buried materials and electromagnetic characteristics of formations are complicated. Therefore, it is very challenging to interpret Lunar Penetrating Radar (LPR) data and segment subsurface layers accurately and reliably. In this paper, deep learning method is utilized to reconstruct geological models from simulated LPR signal dataset. First, the geometric contours of lunar rock are extracted based on the photos of the lunar rock samples from Apollo 14, using image edge detection. The principal component analysis method is used to reduce the dimensionality of LPR data. Then, using the back propagation algorithm based on Root Mean Square prop (RMSprop), an artificial neural network is built to predict geometric characteristics of single buried basaltic rock. The results show that the depth of the buried rock with high-contrast dielectric constant and complex geometric features has been predicted with high accuracies, with the R-square of 0.93. Also, an artificial neural network model is also created to reconstruct geometric characteristics of heterogeneous model with randomly distributed lunar rocks. The preliminary results provide an initial attempt for development of data-driven subsurface imaging techniques in the geoscience field.
The subsurface structure and composition of moon are always heterogeneous, also, both geometric shape of buried materials and electromagnetic characteristics of formations are complicated. Therefore, it is very challenging to interpret Lunar Penetrating Radar (LPR) data and segment subsurface layers accurately and reliably. In this paper, deep learning method is utilized to reconstruct geological models from simulated LPR signal dataset. First, the geometric contours of lunar rock are extracted based on the photos of the lunar rock samples from Apollo 14, using image edge detection. The principal component analysis method is used to reduce the dimensionality of LPR data. Then, using the back propagation algorithm based on Root Mean Square prop (RMSprop), an artificial neural network is built to predict geometric characteristics of single buried basaltic rock. The results show that the depth of the buried rock with high-contrast dielectric constant and complex geometric features has been predicted with high accuracies, with the R-square of 0.93. Also, an artificial neural network model is also created to reconstruct geometric characteristics of heterogeneous model with randomly distributed lunar rocks. The preliminary results provide an initial attempt for development of data-driven subsurface imaging techniques in the geoscience field.
2022, 44(4): 1231-1240.
doi: 10.11999/JEIT210602
Abstract:
In order to estimate the micro-motion parameters accurately and fleetly, an Ultra Wide Band (UWB) scattering center extraction algorithm based on Geometrical Theory of Diffraction (GTD) model and improved matrix pencil is proposed. The radial distance of the scattering center, the type parameters and the scattering intensity can be estimated simultaneously. The target GTD scattering model under UWB condition is transformed into a state space equation in this method, and the singular value decomposition is used to remove the noise component from the Hankel matrix. The generalized eigenvalue decomposition of the reduced Hankel matrix is performed, and the echo estimation is constructed by using the strongest scattering points in a single pulse, and then the radial distance estimation is obtained. Under the condition that the distance parameters are accurately estimated, the model parameters are decoupled so that the type parameters are separated from other parameters, and the type parameters are estimated by the least square algorithm and the search algorithm. Finally, the scattering intensity of the scattering center is estimated based on the least square method. The simulation results show that the improved matrix beam method has good robustness under low SNR, and can extract the target micro-motion distance, type parameters and scattering intensity with high precision.
In order to estimate the micro-motion parameters accurately and fleetly, an Ultra Wide Band (UWB) scattering center extraction algorithm based on Geometrical Theory of Diffraction (GTD) model and improved matrix pencil is proposed. The radial distance of the scattering center, the type parameters and the scattering intensity can be estimated simultaneously. The target GTD scattering model under UWB condition is transformed into a state space equation in this method, and the singular value decomposition is used to remove the noise component from the Hankel matrix. The generalized eigenvalue decomposition of the reduced Hankel matrix is performed, and the echo estimation is constructed by using the strongest scattering points in a single pulse, and then the radial distance estimation is obtained. Under the condition that the distance parameters are accurately estimated, the model parameters are decoupled so that the type parameters are separated from other parameters, and the type parameters are estimated by the least square algorithm and the search algorithm. Finally, the scattering intensity of the scattering center is estimated based on the least square method. The simulation results show that the improved matrix beam method has good robustness under low SNR, and can extract the target micro-motion distance, type parameters and scattering intensity with high precision.
2022, 44(4): 1241-1248.
doi: 10.11999/JEIT211042
Abstract:
The detection of respiration signal under the ruins is of great significance to earthquake rescue. In reality, the human respiration signal behind the obstacle (such as walls) will be masked by noise in the environment. How to improve the Signal-to-Noise Ratio (SNR) of the through-wall respiration signal is still a challenging task. A detection algorithm based on a priori SNR estimation for enhancing the output SNR of the weak through-wall respiration signal is proposed in this paper. Based on the typical Decision-Directed (DD) algorithm of spectral subtraction methods, an adaptive weighting factor is added in the proposed algorithm to eliminate further the residual random noise by reducing the estimation error of the a priori SNR. The performance of the proposed algorithm is investigated through simulation and experimental verification. The output SNR of the proposed respiration detection algorithm is improved compared with the traditional Fast Fourier Transform (FFT), Singular Value Decomposition (SVD), and DD detection algorithm.
The detection of respiration signal under the ruins is of great significance to earthquake rescue. In reality, the human respiration signal behind the obstacle (such as walls) will be masked by noise in the environment. How to improve the Signal-to-Noise Ratio (SNR) of the through-wall respiration signal is still a challenging task. A detection algorithm based on a priori SNR estimation for enhancing the output SNR of the weak through-wall respiration signal is proposed in this paper. Based on the typical Decision-Directed (DD) algorithm of spectral subtraction methods, an adaptive weighting factor is added in the proposed algorithm to eliminate further the residual random noise by reducing the estimation error of the a priori SNR. The performance of the proposed algorithm is investigated through simulation and experimental verification. The output SNR of the proposed respiration detection algorithm is improved compared with the traditional Fast Fourier Transform (FFT), Singular Value Decomposition (SVD), and DD detection algorithm.
2022, 44(4): 1249-1256.
doi: 10.11999/JEIT211217
Abstract:
A new algorithm for imaging of ice-sounding radar is proposed, which can obtain high-resolution subglacial profiles with high processing efficiency. This algorithm, which is a processing algorithm based on fast back-projection for imaging of Ultra-WideBand(UWB) ice-sounding radar, can correct the distance between radar and targets in multilayer media and the geometric variation of range migration caused by multilayer media. The theory of the algorithm is analyzed and the implementation steps of the algorithm are given in this paper, and the algorithm is applied to point targets simulation and ice-sounding data experiment, which verifies the effectiveness of the algorithm in imaging of ice-sounding data. Furthermore, the results of the proposed algorithm with those of the existing ice-sounding radar processing algorithms are compared in three aspect of azimuth clutter suppression ability, computing time and azimuth resolution ability, which verifies this algorithm can effectively improve the computational efficiency without reducing the azimuth clutter suppression ability and azimuth resolution.
A new algorithm for imaging of ice-sounding radar is proposed, which can obtain high-resolution subglacial profiles with high processing efficiency. This algorithm, which is a processing algorithm based on fast back-projection for imaging of Ultra-WideBand(UWB) ice-sounding radar, can correct the distance between radar and targets in multilayer media and the geometric variation of range migration caused by multilayer media. The theory of the algorithm is analyzed and the implementation steps of the algorithm are given in this paper, and the algorithm is applied to point targets simulation and ice-sounding data experiment, which verifies the effectiveness of the algorithm in imaging of ice-sounding data. Furthermore, the results of the proposed algorithm with those of the existing ice-sounding radar processing algorithms are compared in three aspect of azimuth clutter suppression ability, computing time and azimuth resolution ability, which verifies this algorithm can effectively improve the computational efficiency without reducing the azimuth clutter suppression ability and azimuth resolution.
2022, 44(4): 1257-1264.
doi: 10.11999/JEIT211213
Abstract:
Ground Penetrating Radar (GPR) has a good prospect in the detection of underground water pipeline leakage. Previous studies have shown that the underground water pipes leakage can form oscillating hyperbolic signals in GPR profile, but its formation mechanism is not clear. To reveal the formation mechanism of GPR signal characteristics after underground pipeline leakage of different materials, physical model test and multi-physics numerical simulation are carried out to analyzes the characteristics of GPR signals before and after leakage, the formation mechanism of oscillating hyperbolic signal of PVC/metal pipe after leakage and the propagation path of electromagnetic wave. The results show that a stratified leakage zone appears around the underground water pipe after leakage, and more interface reflections and multiple waves bouncing between interfaces occur during the propagation of electromagnetic waves. After the leakage of PVC pipe, creeping wave signals and the reflected signals at the top and bottom of the PVC pipe are reflected many times in the leakage zone to form complex oscillatory hyperbolic signals, while there are multiple reflections between the pipe outer wall and the leakage zone after the metal pipe leakage. The results are helpful to promote the practical applications of GPR to underground pipeline leakage detection and related data interpretation.
Ground Penetrating Radar (GPR) has a good prospect in the detection of underground water pipeline leakage. Previous studies have shown that the underground water pipes leakage can form oscillating hyperbolic signals in GPR profile, but its formation mechanism is not clear. To reveal the formation mechanism of GPR signal characteristics after underground pipeline leakage of different materials, physical model test and multi-physics numerical simulation are carried out to analyzes the characteristics of GPR signals before and after leakage, the formation mechanism of oscillating hyperbolic signal of PVC/metal pipe after leakage and the propagation path of electromagnetic wave. The results show that a stratified leakage zone appears around the underground water pipe after leakage, and more interface reflections and multiple waves bouncing between interfaces occur during the propagation of electromagnetic waves. After the leakage of PVC pipe, creeping wave signals and the reflected signals at the top and bottom of the PVC pipe are reflected many times in the leakage zone to form complex oscillatory hyperbolic signals, while there are multiple reflections between the pipe outer wall and the leakage zone after the metal pipe leakage. The results are helpful to promote the practical applications of GPR to underground pipeline leakage detection and related data interpretation.
2022, 44(4): 1265-1273.
doi: 10.11999/JEIT211072
Abstract:
Under harsh conditions, such as rain, snow, dust, strong light, and dark night, the vision and laser sensors commonly used in autonomous driving solutions may fail because they can not accurately sense the external environment. Therefore, a method for vehicle localization using underground target features sensed by deep learning assisted ground penetrating radar is proposed in this paper. The proposed method is divided into two phases: offline mapping phase and online localization phase. In the offline mapping phase, the ground penetrating radar is used to collect the echo data from the underground targets first, then the Deep Convolutional Neural Network (DCNN) is utilized to extract the target features from the collected echo data, and the extracted target features are saved with the current geographic location information to form a fingerprint map of underground target features. In the localization phase, the DCNN is used to extract the target features from the current echo data collected by the ground penetrating radar first, and then the target feature most similar to the current extracted target feature in the fingerprint map of underground target features is retrieved based on the particle swarm optimization method, and the geographic location information of the retrieved feature is marked as the vehicle localization result by the ground penetrating radar. Finally, the Kalman filter is used to fuse the ground penetrating radar localization result and the mileage information measured by the ranging wheel to obtain a high-precision localization result. The localization performance of the proposed localization method is tested on the experimental scenario with rich underground targets and the actual urban road scenario. The experimental results show that, compared with the single raw data-based ground penetrating radar localization method, the deep learning assisted ground penetrating radar localization method can avoid directly calculating the similarity between the raw radar data, reduce the amount of data computation and data transmission, and has the real-time localization capability. At the same time, the fingerprint map of underground target features is robust to the changes of the raw radar data, so the average localization error of the proposed method is reduced by about 70%. The deep learning assisted ground penetrating radar localization method can be used as a supplement to the detection and localization method of autonomous vehicles in harsh environments in the future.
Under harsh conditions, such as rain, snow, dust, strong light, and dark night, the vision and laser sensors commonly used in autonomous driving solutions may fail because they can not accurately sense the external environment. Therefore, a method for vehicle localization using underground target features sensed by deep learning assisted ground penetrating radar is proposed in this paper. The proposed method is divided into two phases: offline mapping phase and online localization phase. In the offline mapping phase, the ground penetrating radar is used to collect the echo data from the underground targets first, then the Deep Convolutional Neural Network (DCNN) is utilized to extract the target features from the collected echo data, and the extracted target features are saved with the current geographic location information to form a fingerprint map of underground target features. In the localization phase, the DCNN is used to extract the target features from the current echo data collected by the ground penetrating radar first, and then the target feature most similar to the current extracted target feature in the fingerprint map of underground target features is retrieved based on the particle swarm optimization method, and the geographic location information of the retrieved feature is marked as the vehicle localization result by the ground penetrating radar. Finally, the Kalman filter is used to fuse the ground penetrating radar localization result and the mileage information measured by the ranging wheel to obtain a high-precision localization result. The localization performance of the proposed localization method is tested on the experimental scenario with rich underground targets and the actual urban road scenario. The experimental results show that, compared with the single raw data-based ground penetrating radar localization method, the deep learning assisted ground penetrating radar localization method can avoid directly calculating the similarity between the raw radar data, reduce the amount of data computation and data transmission, and has the real-time localization capability. At the same time, the fingerprint map of underground target features is robust to the changes of the raw radar data, so the average localization error of the proposed method is reduced by about 70%. The deep learning assisted ground penetrating radar localization method can be used as a supplement to the detection and localization method of autonomous vehicles in harsh environments in the future.
2022, 44(4): 1274-1283.
doi: 10.11999/JEIT211221
Abstract:
To solve the problems of data preprocessing and feature utilization in the existing work of gesture recognition of radio frequency signals, a gesture recognition algorithm for spatio-temporal compressed feature representation learning of Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed. First, static interference removal and moving target point filtering are performed on the Range-Doppler (RD) image of the FMCW radar echo reflected by the hand, which could reduce the interference of clutter on the gesture signal, and also reduce greatly the calculation of the data. Then, a method for compressing the spatial-temporal features of gesture is adopted to realize the compression mapping of multidimensional features using the dominant velocity of the moving target point to represent the motion characteristics of the gesture, which includes the key feature information of the gesture motion. Finally, a single channel Convolutional Neural Network (CNN) is designed to learn and classify multidimensional gesture feature information in multi-user and multi-location gesture application scenes. Experimental results show that the proposed gesture recognition method has significant performance in recognition accuracy, real-time performance and generalization ability.
To solve the problems of data preprocessing and feature utilization in the existing work of gesture recognition of radio frequency signals, a gesture recognition algorithm for spatio-temporal compressed feature representation learning of Frequency Modulated Continuous Wave (FMCW) millimeter wave radar is proposed. First, static interference removal and moving target point filtering are performed on the Range-Doppler (RD) image of the FMCW radar echo reflected by the hand, which could reduce the interference of clutter on the gesture signal, and also reduce greatly the calculation of the data. Then, a method for compressing the spatial-temporal features of gesture is adopted to realize the compression mapping of multidimensional features using the dominant velocity of the moving target point to represent the motion characteristics of the gesture, which includes the key feature information of the gesture motion. Finally, a single channel Convolutional Neural Network (CNN) is designed to learn and classify multidimensional gesture feature information in multi-user and multi-location gesture application scenes. Experimental results show that the proposed gesture recognition method has significant performance in recognition accuracy, real-time performance and generalization ability.
2022, 44(4): 1284-1294.
doi: 10.11999/JEIT211032
Abstract:
To solve the problem of the difficulty of target feature extraction and low recognition accuracy in Ground Penetrating Radar (GPR) two-dimensional profile, a deep learning method is used to extract the characteristic hyperbola of targets in B-SCAN image. Physics-based of GPR, a cascade Convolutional Neural Network (CNN) is designed to detect and remove the direct wave interference signal in the echo data. Then, the B-SCAN image is obtained by CNN, and the characteristic signals are classified and recognized to extract the characteristic hyperbola of the target. Meanwhile, in order to deal with the problem that the interference signals affect the structural integrity of the feature hyperbola, a feature data completion method based on directional guidance is proposed to improve the accuracy of the feature hyperbola recognition results. Compared with Histogram of Oriented Gradients(HOG) algorithm, You Only Look Once V3(YOLOV3) algorithm and Faster Region-based Convolutional Neural Network(Faster RCNN) algorithm, the detection result of the proposed method is the best in the comprehensive evaluation index F.
To solve the problem of the difficulty of target feature extraction and low recognition accuracy in Ground Penetrating Radar (GPR) two-dimensional profile, a deep learning method is used to extract the characteristic hyperbola of targets in B-SCAN image. Physics-based of GPR, a cascade Convolutional Neural Network (CNN) is designed to detect and remove the direct wave interference signal in the echo data. Then, the B-SCAN image is obtained by CNN, and the characteristic signals are classified and recognized to extract the characteristic hyperbola of the target. Meanwhile, in order to deal with the problem that the interference signals affect the structural integrity of the feature hyperbola, a feature data completion method based on directional guidance is proposed to improve the accuracy of the feature hyperbola recognition results. Compared with Histogram of Oriented Gradients(HOG) algorithm, You Only Look Once V3(YOLOV3) algorithm and Faster Region-based Convolutional Neural Network(Faster RCNN) algorithm, the detection result of the proposed method is the best in the comprehensive evaluation index F.
2022, 44(4): 1295-1302.
doi: 10.11999/JEIT211193
Abstract:
In the linear Frequency Modulation Continuous Waveform (FMCW) Radar system, due to the non-ideal characteristics of each instrument, the phase of the signal produces various distortions, which affects seriously the radar's ranging accuracy and imaging quality. It requires certain correction methods to obtain high-precision measurement results. In view of the characteristics of FMCW signal and deterministic nonlinear phase, this paper establishes a FMCW signal model subject to nonlinear interference, and proposes a new nonlinear estimation method based on difference filtering, which can simultaneously estimate periodic and non-periodic nonlinearities, and uses Matching Fourier Transform (MFT) method to correct the nonlinear phase. Simulation and comparative analysis show that this method has higher estimation accuracy than other methods, and it can also have a good correction effect when the nonlinearity is large. Finally, the actual measurement data of the radar is used to verify the effectiveness of the algorithm.
In the linear Frequency Modulation Continuous Waveform (FMCW) Radar system, due to the non-ideal characteristics of each instrument, the phase of the signal produces various distortions, which affects seriously the radar's ranging accuracy and imaging quality. It requires certain correction methods to obtain high-precision measurement results. In view of the characteristics of FMCW signal and deterministic nonlinear phase, this paper establishes a FMCW signal model subject to nonlinear interference, and proposes a new nonlinear estimation method based on difference filtering, which can simultaneously estimate periodic and non-periodic nonlinearities, and uses Matching Fourier Transform (MFT) method to correct the nonlinear phase. Simulation and comparative analysis show that this method has higher estimation accuracy than other methods, and it can also have a good correction effect when the nonlinearity is large. Finally, the actual measurement data of the radar is used to verify the effectiveness of the algorithm.
2022, 44(4): 1303-1310.
doi: 10.11999/JEIT210676
Abstract:
In through-the-wall imaging, the clutter can not be eliminated completely through traditional algorithms, and affects seriously the subsequent target detection and recognition. To solve the problem, based on robust principal component analysis theory, a joint low-rank and sparse model is established in echo and image domain respectively. The models are solved by Smoothing Fast Alternating Linearization (SFAL) method. Then, the target images are dealt with exponentially weighted multiply multi-domain image fusion to obtain the final image. The simulation results indicate that the algorithm has great speed and accuracy with effective improvement on imaging quality of targets.
In through-the-wall imaging, the clutter can not be eliminated completely through traditional algorithms, and affects seriously the subsequent target detection and recognition. To solve the problem, based on robust principal component analysis theory, a joint low-rank and sparse model is established in echo and image domain respectively. The models are solved by Smoothing Fast Alternating Linearization (SFAL) method. Then, the target images are dealt with exponentially weighted multiply multi-domain image fusion to obtain the final image. The simulation results indicate that the algorithm has great speed and accuracy with effective improvement on imaging quality of targets.
2022, 44(4): 1311-1317.
doi: 10.11999/JEIT210410
Abstract:
The density-depth relationship of the Antarctic ice sheet is one of the key parameter when building the high-precision surface mass balance model. An ice sheet density and permittivity inversion method is proposed based on Frequency Modulated Continuous Wave (FMCW) radar. The density profile is preliminarily inverted firstly by the layer stripping inversion method. Then the density model is established by the shallow inversion density and the ice sheet densification empirical equation. Finally, the optimal model parameters are found through the optimization algorithm to calibration the density. Experimental results based on theoretical ice sheet models and measured radar data verify the effectiveness of the new method.
The density-depth relationship of the Antarctic ice sheet is one of the key parameter when building the high-precision surface mass balance model. An ice sheet density and permittivity inversion method is proposed based on Frequency Modulated Continuous Wave (FMCW) radar. The density profile is preliminarily inverted firstly by the layer stripping inversion method. Then the density model is established by the shallow inversion density and the ice sheet densification empirical equation. Finally, the optimal model parameters are found through the optimization algorithm to calibration the density. Experimental results based on theoretical ice sheet models and measured radar data verify the effectiveness of the new method.
2022, 44(4): 1318-1326.
doi: 10.11999/JEIT211073
Abstract:
In order to solve the problem that the Frequency Modulated Continuous Wave (FMCW) radar could not guarantee the detection accuracy of life signal, a life signal extraction method based on multichannel FMCW radar is proposed in this paper. After the proposed method achieves the reconstruction of the range profiles and the extraction of phase signal corresponding to each equivalent receiving channel, the Maximum Ratio Combining (MRC) technique is used to fuse the phase signals from the multiple receiving channels. Then the Variational Mode Decomposition (VMD) is adopted to decompose the fused phase signal to reconstruct the life signal. Finally, the Fast Fourier Transform (FFT) is performed on the reconstructed the life signal to obtain the respiration rate and heartbeat rate. The experimental results show that the proposed life signal extraction method based on multichannel FMCW radar can extract the life signal more robustly and accurately compared to single channel FMCW radar. In addition, the combined MRC and VMD signal processing method outperforms the combined MRC and Band-Pass Filter (BPF) method.
In order to solve the problem that the Frequency Modulated Continuous Wave (FMCW) radar could not guarantee the detection accuracy of life signal, a life signal extraction method based on multichannel FMCW radar is proposed in this paper. After the proposed method achieves the reconstruction of the range profiles and the extraction of phase signal corresponding to each equivalent receiving channel, the Maximum Ratio Combining (MRC) technique is used to fuse the phase signals from the multiple receiving channels. Then the Variational Mode Decomposition (VMD) is adopted to decompose the fused phase signal to reconstruct the life signal. Finally, the Fast Fourier Transform (FFT) is performed on the reconstructed the life signal to obtain the respiration rate and heartbeat rate. The experimental results show that the proposed life signal extraction method based on multichannel FMCW radar can extract the life signal more robustly and accurately compared to single channel FMCW radar. In addition, the combined MRC and VMD signal processing method outperforms the combined MRC and Band-Pass Filter (BPF) method.
2022, 44(4): 1327-1335.
doi: 10.11999/JEIT211108
Abstract:
To reduce the insertion loss of the filter and achieve filter miniaturization, a novel Spoof Surface Plasmon Polaritons (SSPPs) excitonic low-pass filter with a notched band is proposed, which consists mainly of novel bow-tie cells structure, transition structures, and InterDigital Capacitance Loaded Loop Resonators (IDCLLR) structures used for accomplishing the notch function. The novel bow-tie cell structure is composed of an elliptical patch rotated 30° to the left and right directions, which can significantly reduce insertion loss after hollowing out. Compared with the traditional rectangular and elliptical structure, novel bow-tie structure has better dispersion characteristics, which improves greatly the filter's in-band flatness and out-of-band rejection capability. In addition, the dispersion curves of different cell structures such as rectangles, ellipses, trapezoids and novel bow-tie are analyzed, and the S21 and S11 curves of filters are simulated. The results show that the novel bow-tie unit structure has advantages in dispersion characteristics, insertion loss, low cut-off frequency and out-of-band suppression. Finally, the filter is processed and tested, and the test results show that the filter simulation results and test results match well, have good out-of-band rejection and in-band flatness, which can achieve notch suppression for specific interference bands. The size of the filter is 0.98λ0×0.17λ0. From the point of view of a new element structure is designed, this SSPPs filter achieves good performance and miniaturization.
To reduce the insertion loss of the filter and achieve filter miniaturization, a novel Spoof Surface Plasmon Polaritons (SSPPs) excitonic low-pass filter with a notched band is proposed, which consists mainly of novel bow-tie cells structure, transition structures, and InterDigital Capacitance Loaded Loop Resonators (IDCLLR) structures used for accomplishing the notch function. The novel bow-tie cell structure is composed of an elliptical patch rotated 30° to the left and right directions, which can significantly reduce insertion loss after hollowing out. Compared with the traditional rectangular and elliptical structure, novel bow-tie structure has better dispersion characteristics, which improves greatly the filter's in-band flatness and out-of-band rejection capability. In addition, the dispersion curves of different cell structures such as rectangles, ellipses, trapezoids and novel bow-tie are analyzed, and the S21 and S11 curves of filters are simulated. The results show that the novel bow-tie unit structure has advantages in dispersion characteristics, insertion loss, low cut-off frequency and out-of-band suppression. Finally, the filter is processed and tested, and the test results show that the filter simulation results and test results match well, have good out-of-band rejection and in-band flatness, which can achieve notch suppression for specific interference bands. The size of the filter is 0.98λ0×0.17λ0. From the point of view of a new element structure is designed, this SSPPs filter achieves good performance and miniaturization.
2022, 44(4): 1336-1342.
doi: 10.11999/JEIT211286
Abstract:
On May 22, 2021, China's first Mars Rover "Zhurong" began its exploration on Mars surface. One of the payloads is Mars Rover Penetrating Radar (RoPeR), which composes of a high frequency channel and a low frequency channel. The scientific object of RoPeR is to reveal the subsurface structures of landing site and detect potentially buried water/dry ice. The high frequency channel is equipped with four Vivaldi antennas working at the frequency band of 0.45～2.15 GHz, which can record radar echo in four different polarization channels. An analogical experiment is performed to analyze the reflection signals from a dry ice and a water ice samples. The experimental results show that dry ice and water ice have different polarization scattering characteristics and\begin{document}$ H - \alpha $\end{document} ![]()
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On May 22, 2021, China's first Mars Rover "Zhurong" began its exploration on Mars surface. One of the payloads is Mars Rover Penetrating Radar (RoPeR), which composes of a high frequency channel and a low frequency channel. The scientific object of RoPeR is to reveal the subsurface structures of landing site and detect potentially buried water/dry ice. The high frequency channel is equipped with four Vivaldi antennas working at the frequency band of 0.45～2.15 GHz, which can record radar echo in four different polarization channels. An analogical experiment is performed to analyze the reflection signals from a dry ice and a water ice samples. The experimental results show that dry ice and water ice have different polarization scattering characteristics and
2022, 44(4): 1343-1350.
doi: 10.11999/JEIT210172
Abstract:
Focusing on the problem that the azimuth position is fixed and the radial velocity can not be controlled when the false moving target generated by the traditional two-dimensional shift-frequency jamming against the dual-channel Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI), a new type of false moving target generation method with shift-frequency modulation based on the cooperation of dual jammers is proposed. This method uses two-dimensional shift-frequency modulation to control the position of the false target and uses dual synergetic jammers to control the radial velocity of the false moving target. The dual jammers coordinate through the amplitude and phase modulation coefficients, which can be solved by solving linear equations. In this paper, the limitation of single jammer shift-frequency jamming against dual-channel SAR-GMTI is analyzed, the dual jammer cooperative deception jamming model is introduced, the method for solving amplitude and phase modulation coefficients is explained, and the steps of generating false moving targets is given. Theoretical analysis and simulation experiment verify the effectiveness of the proposed method. And in the process of generating false moving targets, only two-dimensional shift-frequency modulation is used, so the method is relatively simple to achieved, and can offer certain reference value for practical engineering applications.
Focusing on the problem that the azimuth position is fixed and the radial velocity can not be controlled when the false moving target generated by the traditional two-dimensional shift-frequency jamming against the dual-channel Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI), a new type of false moving target generation method with shift-frequency modulation based on the cooperation of dual jammers is proposed. This method uses two-dimensional shift-frequency modulation to control the position of the false target and uses dual synergetic jammers to control the radial velocity of the false moving target. The dual jammers coordinate through the amplitude and phase modulation coefficients, which can be solved by solving linear equations. In this paper, the limitation of single jammer shift-frequency jamming against dual-channel SAR-GMTI is analyzed, the dual jammer cooperative deception jamming model is introduced, the method for solving amplitude and phase modulation coefficients is explained, and the steps of generating false moving targets is given. Theoretical analysis and simulation experiment verify the effectiveness of the proposed method. And in the process of generating false moving targets, only two-dimensional shift-frequency modulation is used, so the method is relatively simple to achieved, and can offer certain reference value for practical engineering applications.
2022, 44(4): 1351-1357.
doi: 10.11999/JEIT210190
Abstract:
Distorted radar electromagnetic signals will seriously affect the detection performance of radar reconnaissance equipment. How to identify effectively the type of distorted signal has important practical significance for the accurate perception of radar systems. For distorted radar signals, there is often a problem of sample scarcity. A Residual Network based on Model-Agnostic Meta-Learning (MAML-ResNet) is proposed. The algorithm first uses normal radar signal samples to train the meta-learner, then the meta-learner is fine-tuned in the distorted signal samples. Finally, the distorted signal is recognized with only a small number of distorted signal samples. Experimental results show that the recognition accuracy of distorted signals under small sample data is effectively improved.
Distorted radar electromagnetic signals will seriously affect the detection performance of radar reconnaissance equipment. How to identify effectively the type of distorted signal has important practical significance for the accurate perception of radar systems. For distorted radar signals, there is often a problem of sample scarcity. A Residual Network based on Model-Agnostic Meta-Learning (MAML-ResNet) is proposed. The algorithm first uses normal radar signal samples to train the meta-learner, then the meta-learner is fine-tuned in the distorted signal samples. Finally, the distorted signal is recognized with only a small number of distorted signal samples. Experimental results show that the recognition accuracy of distorted signals under small sample data is effectively improved.
2022, 44(4): 1358-1365.
doi: 10.11999/JEIT210180
Abstract:
For the radar signals simulations of time-varying ocean surface, a radar signal model of sea clutter incorporating the electromagnetic scattering mechanism and radar signal modulation characteristics is developed. First, a set of time-varying sea surface geometric samples are constructed, and an efficient simulation of sea scattering at each time is performed by accelerating the calculation of the improved facet-based two-scale model, and the accuracy of the electromagnetic calculation method is demonstrated by comparing with the measured data. Then the band-wide echo signal model is established in the form of sub-pulse, and the scattering data of each surface element is replaced with its complex amplitude to complete the radar signal modeling. In simulations, the rationality and efficiency of the electromagnetic model are verified, and the statistical analysis of sea clutter data shows the effect of pulse compression on clutter suppression. This sea clutter model not only takes into account of the complex scattering mechanism and motion characteristics of the ocean, but also has the form of a general band-wide signal, so it can provide comprehensive source data for the interpretation of ocean scattering phenomena and the analysis of signal processing algorithms.
For the radar signals simulations of time-varying ocean surface, a radar signal model of sea clutter incorporating the electromagnetic scattering mechanism and radar signal modulation characteristics is developed. First, a set of time-varying sea surface geometric samples are constructed, and an efficient simulation of sea scattering at each time is performed by accelerating the calculation of the improved facet-based two-scale model, and the accuracy of the electromagnetic calculation method is demonstrated by comparing with the measured data. Then the band-wide echo signal model is established in the form of sub-pulse, and the scattering data of each surface element is replaced with its complex amplitude to complete the radar signal modeling. In simulations, the rationality and efficiency of the electromagnetic model are verified, and the statistical analysis of sea clutter data shows the effect of pulse compression on clutter suppression. This sea clutter model not only takes into account of the complex scattering mechanism and motion characteristics of the ocean, but also has the form of a general band-wide signal, so it can provide comprehensive source data for the interpretation of ocean scattering phenomena and the analysis of signal processing algorithms.
2022, 44(4): 1373-1381.
doi: 10.11999/JEIT210134
Abstract:
In order to reduce the damage to carrier aerodynamics by the antenna and improve the performance of the antenna in a complex electromagnetic environment, this paper designs a circularly polarized reconfigurable end-fire antenna. The antenna is based on the Substrate Integrated Waveguide (SIW) horn antenna. The front end of the horn is loaded with a phase shifter and butterfly dipoles, and four MEMS switches are loaded between the upper and lower butterfly dipoles. The current flow path can be controlled by the on and off of the switch, so that the antenna radiates two circularly polarized waves respectively. The measured results show that by controlling the on-off combination of the switch, the designed antenna can radiate Left-Hand Circular Polarization (LHCP) or Right-Hand Circular Polarization (RHCP) electromagnetic waves in the 11.24～11.83 GHz frequency band along the end-fire direction. The measurement results are basically consistent with the simulation results, verifying the effectiveness of the design.
In order to reduce the damage to carrier aerodynamics by the antenna and improve the performance of the antenna in a complex electromagnetic environment, this paper designs a circularly polarized reconfigurable end-fire antenna. The antenna is based on the Substrate Integrated Waveguide (SIW) horn antenna. The front end of the horn is loaded with a phase shifter and butterfly dipoles, and four MEMS switches are loaded between the upper and lower butterfly dipoles. The current flow path can be controlled by the on and off of the switch, so that the antenna radiates two circularly polarized waves respectively. The measured results show that by controlling the on-off combination of the switch, the designed antenna can radiate Left-Hand Circular Polarization (LHCP) or Right-Hand Circular Polarization (RHCP) electromagnetic waves in the 11.24～11.83 GHz frequency band along the end-fire direction. The measurement results are basically consistent with the simulation results, verifying the effectiveness of the design.
2022, 44(4): 1382-1387.
doi: 10.11999/JEIT210111
Abstract:
To improve the Y-Gd-Hf-O cathodes anti-electron bombardment ability, a scandia doped cathode is prepared by a pressing technique combined with sintering in hydrogen atmosphere. The tested result shows that the emitting current from the cathode operating at 1550 °C can remain to 87.5% of the initial one after continuous electron bombardment of 10 W for 480 h, reflecting a better anti-electron bombardment capability. The surface microstructure analysis result indicates that a cermet structure has been formed. A n-type semiconductor Y2O3-x layer has generated on the cathode surface after being sintered and activated at high temperature, which is favorable for enhancing the thermionic emission, improving the surface conductivity, and lowering the work function.
To improve the Y-Gd-Hf-O cathodes anti-electron bombardment ability, a scandia doped cathode is prepared by a pressing technique combined with sintering in hydrogen atmosphere. The tested result shows that the emitting current from the cathode operating at 1550 °C can remain to 87.5% of the initial one after continuous electron bombardment of 10 W for 480 h, reflecting a better anti-electron bombardment capability. The surface microstructure analysis result indicates that a cermet structure has been formed. A n-type semiconductor Y2O3-x layer has generated on the cathode surface after being sintered and activated at high temperature, which is favorable for enhancing the thermionic emission, improving the surface conductivity, and lowering the work function.
2022, 44(4): 1388-1394.
doi: 10.11999/JEIT210174
Abstract:
In adaptive array processing for Global Navigation Satellite System (GNSS) anti-jamming, system noise is usually modeled as AWGN(Additive White Gauss Noise). But in engineering, system noise is generally non-white and colored noise has a great impact on the performance of STAP (Space-Time Adaptive Processing) algorithm. Firstly, the theoretical relationship between the power spectrum of noise and the correlation peak of GNSS signal after space-time anti-jamming is derived, then the theoretical relationship is verified by simulation experiment which simultaneously analyzes the effect of equivalent bandwidth and spectrum peak shift of noise on GNSS signal’s correlation peak. Experimental results show the more the noise power spectrum energy is concentrated at the peak of the power spectrum of GNSS signal, the lower the correlation peak of the GNSS signal after space-time anti-jamming. For the above problem, diagonal loading and subspace projection methods are proposed in this paper, and the two methods achieve the whitening of colored noise by consistent processing of the eigenvalues of the noise covariance matrix, so that the effect of colored noise is eliminated. Finally, method is verified by experiments.
In adaptive array processing for Global Navigation Satellite System (GNSS) anti-jamming, system noise is usually modeled as AWGN(Additive White Gauss Noise). But in engineering, system noise is generally non-white and colored noise has a great impact on the performance of STAP (Space-Time Adaptive Processing) algorithm. Firstly, the theoretical relationship between the power spectrum of noise and the correlation peak of GNSS signal after space-time anti-jamming is derived, then the theoretical relationship is verified by simulation experiment which simultaneously analyzes the effect of equivalent bandwidth and spectrum peak shift of noise on GNSS signal’s correlation peak. Experimental results show the more the noise power spectrum energy is concentrated at the peak of the power spectrum of GNSS signal, the lower the correlation peak of the GNSS signal after space-time anti-jamming. For the above problem, diagonal loading and subspace projection methods are proposed in this paper, and the two methods achieve the whitening of colored noise by consistent processing of the eigenvalues of the noise covariance matrix, so that the effect of colored noise is eliminated. Finally, method is verified by experiments.
2022, 44(4): 1395-1401.
doi: 10.11999/JEIT210024
Abstract:
At the Co-frequency and Co-time Full Duplex (CCFD) multi-carrier signal time asynchronous scenario,, the maximum multipath time offset between the Signal of Interest (SoI) and the Self-Interference (SI) exceeds the length of the Cyclic Prefix (CP), which causes the decrease of the performance in the frequency domain self-interference cancellation. In view of this problem, a timing asynchronous self-interference suppression method by segment convolution is proposed; The relationship between the SI and the SoI is analyzed; The SI suppression process is established. Theoretical and simulation results show that the cancellation performance in the asynchronous state is equivalent to that in the synchronous state.
At the Co-frequency and Co-time Full Duplex (CCFD) multi-carrier signal time asynchronous scenario,, the maximum multipath time offset between the Signal of Interest (SoI) and the Self-Interference (SI) exceeds the length of the Cyclic Prefix (CP), which causes the decrease of the performance in the frequency domain self-interference cancellation. In view of this problem, a timing asynchronous self-interference suppression method by segment convolution is proposed; The relationship between the SI and the SoI is analyzed; The SI suppression process is established. Theoretical and simulation results show that the cancellation performance in the asynchronous state is equivalent to that in the synchronous state.
2022, 44(4): 1402-1411.
doi: 10.11999/JEIT210293
Abstract:
In order to solve the problem of mass access and how to improve spectrum efficiency in 5G massive Machine-Type Communication (mMTC) scenario, for the uplink grant-free Non-Orthogonal Multi-Access(NOMA) system, new sets of non-orthogonal binary spreading sequences with low Peak Average Power Ratio(PAPR) and lengths non-power-of-two is proposed by inserting elements into binary Golay sequences using insertion function. Simulation results confirm that the resulting sequence sets has low coherence, which provides reliable performance for active user detection based on Compressed Sensing (CS). Compared with the traditional Zadoff-chu sequences, the new binary sequence sets has a smaller alphabet set, which is easy to implement. Moreover, the resultanted sequences exhibit the PAPR of at most 4, which is lower than those for Gaussian and Zadoff-Chu sequences. Therefore, the problem of high peak-to-power ratio in time domain can be solved effectively.
In order to solve the problem of mass access and how to improve spectrum efficiency in 5G massive Machine-Type Communication (mMTC) scenario, for the uplink grant-free Non-Orthogonal Multi-Access(NOMA) system, new sets of non-orthogonal binary spreading sequences with low Peak Average Power Ratio(PAPR) and lengths non-power-of-two is proposed by inserting elements into binary Golay sequences using insertion function. Simulation results confirm that the resulting sequence sets has low coherence, which provides reliable performance for active user detection based on Compressed Sensing (CS). Compared with the traditional Zadoff-chu sequences, the new binary sequence sets has a smaller alphabet set, which is easy to implement. Moreover, the resultanted sequences exhibit the PAPR of at most 4, which is lower than those for Gaussian and Zadoff-Chu sequences. Therefore, the problem of high peak-to-power ratio in time domain can be solved effectively.
2022, 44(4): 1412-1420.
doi: 10.11999/JEIT210248
Abstract:
In order to overcome the shortcomings of the existing non-cooperative self-synchronous scramblers recognition algorithms with low recognition rate and poor adaptability under low signal-to-noise ratio, a blind recognition method of self-synchronous scramblers based on cosine conformity is proposed. Firstly, based on the source imbalance and self-synchronous scramblers descrambling principle, the error-containing check equation of the self-synchronous scramblers is established, and then the received soft decision sequence is converted into the posterior probability sequence of the information symbol. The possible generating polynomials are traversed. The cosine conformity is introduced as a statistic in the traversal process, and the optimal discrimination threshold is solved by analyzing the statistical characteristics of the cosine conformity. The self-synchronous scramblers generating polynomial is identified according to the relationship between the statistics and the discrimination threshold. The simulation results show that the algorithm can effectively identify the generating polynomial, and the recognition rate is better than the existing algorithm under low signal-to-noise ratio, and it has good low signal-to-noise ratio adaptability. When the source imbalance is 0.1, the length of the intercepted scrambling code sequence is 800 bit and 0.05, and the length of the intercepted scrambling code sequence is 3000 bit, the identification of the generating polynomial can be effectively completed. Compared with the current algorithm, the recognition performance of this algorithm is better than the existing hard decision. The performance of the algorithm is improved by 1～2 dB compared with the hard decision algorithm.
In order to overcome the shortcomings of the existing non-cooperative self-synchronous scramblers recognition algorithms with low recognition rate and poor adaptability under low signal-to-noise ratio, a blind recognition method of self-synchronous scramblers based on cosine conformity is proposed. Firstly, based on the source imbalance and self-synchronous scramblers descrambling principle, the error-containing check equation of the self-synchronous scramblers is established, and then the received soft decision sequence is converted into the posterior probability sequence of the information symbol. The possible generating polynomials are traversed. The cosine conformity is introduced as a statistic in the traversal process, and the optimal discrimination threshold is solved by analyzing the statistical characteristics of the cosine conformity. The self-synchronous scramblers generating polynomial is identified according to the relationship between the statistics and the discrimination threshold. The simulation results show that the algorithm can effectively identify the generating polynomial, and the recognition rate is better than the existing algorithm under low signal-to-noise ratio, and it has good low signal-to-noise ratio adaptability. When the source imbalance is 0.1, the length of the intercepted scrambling code sequence is 800 bit and 0.05, and the length of the intercepted scrambling code sequence is 3000 bit, the identification of the generating polynomial can be effectively completed. Compared with the current algorithm, the recognition performance of this algorithm is better than the existing hard decision. The performance of the algorithm is improved by 1～2 dB compared with the hard decision algorithm.
2022, 44(4): 1421-1428.
doi: 10.11999/JEIT210195
Abstract:
In 5G mobile communication, the introduction of Network Slice (NS) solves successfully the problem of uneven network resource allocation in different business scenarios. In view of the problem that traditional algorithms can not meet the security deployment of 5G network multi business scenario slicing, an end-to-end Network Slicing security Requirement (NSR) deployment algorithm for multi business scenario is proposed. Firstly, the security of nodes in slice deployment process is defined; Secondly, the nodes are sorted and mapped according to their security. On this basis, in order to minimize the cost of network resource deployment and improve the security benefits of deployment, a mathematical model of slice deployment is constructed; Finally, considering the different resource requirements of each type of slicing, a targeted deployment algorithm is proposed to realize the secure deployment of end-to-end network slicing. Simulation results show that the proposed algorithm can satisfy the security deployment of end-to-end network slices, reduce the deployment cost and obtain better deployment security benefits.
In 5G mobile communication, the introduction of Network Slice (NS) solves successfully the problem of uneven network resource allocation in different business scenarios. In view of the problem that traditional algorithms can not meet the security deployment of 5G network multi business scenario slicing, an end-to-end Network Slicing security Requirement (NSR) deployment algorithm for multi business scenario is proposed. Firstly, the security of nodes in slice deployment process is defined; Secondly, the nodes are sorted and mapped according to their security. On this basis, in order to minimize the cost of network resource deployment and improve the security benefits of deployment, a mathematical model of slice deployment is constructed; Finally, considering the different resource requirements of each type of slicing, a targeted deployment algorithm is proposed to realize the secure deployment of end-to-end network slicing. Simulation results show that the proposed algorithm can satisfy the security deployment of end-to-end network slices, reduce the deployment cost and obtain better deployment security benefits.
2022, 44(4): 1429-1439.
doi: 10.11999/JEIT210033
Abstract:
In view of the fact that the current uncertainty similarity measurement based on cloud model is either the quantitative calculation of accurate local data, or the measurement only through its overall geometric features, which leads to the result with great one sidedness. In this paper, considering the overall geometric features of cloud model and the contribution of micro cloud droplets distribution, an uncertainty similarity measurement method based on the fusion of distribution contour and local features is proposed, i.e. the Envelope Area of the Contribution based on Cloud Model (EACCM). In this method, the envelope (the area between the inner envelope curve and the outer envelope curve) which reflects the geometric characteristics of the cloud model is used as the basis of similarity measurement, and a comprehensive measurement model is established based on the contribution of cloud droplets contained in the overlapped part. The simulation results show that the method is more scientific and reasonable, and can effectively avoid the problem of abnormally similarity caused by the large difference or very close of the same digital features.
In view of the fact that the current uncertainty similarity measurement based on cloud model is either the quantitative calculation of accurate local data, or the measurement only through its overall geometric features, which leads to the result with great one sidedness. In this paper, considering the overall geometric features of cloud model and the contribution of micro cloud droplets distribution, an uncertainty similarity measurement method based on the fusion of distribution contour and local features is proposed, i.e. the Envelope Area of the Contribution based on Cloud Model (EACCM). In this method, the envelope (the area between the inner envelope curve and the outer envelope curve) which reflects the geometric characteristics of the cloud model is used as the basis of similarity measurement, and a comprehensive measurement model is established based on the contribution of cloud droplets contained in the overlapped part. The simulation results show that the method is more scientific and reasonable, and can effectively avoid the problem of abnormally similarity caused by the large difference or very close of the same digital features.
2022, 44(4): 1440-1452.
doi: 10.11999/JEIT210019
Abstract:
Link prediction aims to discover missing connected edges and possible future interaction in complex networks. The evolution mechanism of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. At present, many methods based on time series analysis are proposed, but the influence of the network evolution process on the network itself is ignored, and the methods based on the static network algorithm only consider the influence of the evolution of edges, which may lead to inadequate utilization of feature information and can not achieve better prediction accuracy. In view of the above problems, a novel Temporal Link Prediction algorithm base on Fusion Local Structure Influence (TLP-FLSI) is proposed, which fuses the impact of local nodes and edges. Firstly, based on the influence of network topology structure, Common Temporal Link Prediction Model(CTLPM)is proposed. Secondly, the evolution mechanism of the interaction between topological entities on the dynamic network is studied, and the evolution factors of nodes and edges, as well as the decay evolution factors of time series are defined respectively, and considering various factors, TLP-FLSI is derivated from CTLPM. Finally, compared with traditional temporal link predict method, including moving average methods, error correction methods, extended weighted method, graph attention methods, experimental results of seven real data sets show that TLP-FLSI achieves great improvement in accuracy and ranking score.
Link prediction aims to discover missing connected edges and possible future interaction in complex networks. The evolution mechanism of temporal networks has gained the attention of researchers with its ubiquitous applications in a variety of real-world scenarios. At present, many methods based on time series analysis are proposed, but the influence of the network evolution process on the network itself is ignored, and the methods based on the static network algorithm only consider the influence of the evolution of edges, which may lead to inadequate utilization of feature information and can not achieve better prediction accuracy. In view of the above problems, a novel Temporal Link Prediction algorithm base on Fusion Local Structure Influence (TLP-FLSI) is proposed, which fuses the impact of local nodes and edges. Firstly, based on the influence of network topology structure, Common Temporal Link Prediction Model(CTLPM)is proposed. Secondly, the evolution mechanism of the interaction between topological entities on the dynamic network is studied, and the evolution factors of nodes and edges, as well as the decay evolution factors of time series are defined respectively, and considering various factors, TLP-FLSI is derivated from CTLPM. Finally, compared with traditional temporal link predict method, including moving average methods, error correction methods, extended weighted method, graph attention methods, experimental results of seven real data sets show that TLP-FLSI achieves great improvement in accuracy and ranking score.
2022, 44(4): 1453-1460.
doi: 10.11999/JEIT210268
Abstract:
The decrease in accuracy of pedestrian detection mainly caused by occlusion and too small scale. Since the pedestrian head is not easily occluded and it’s bounding box contains less background interference, a multi-feature fusion pedestrian detection method combines head and overall information is proposed. Firstly, a feature pyramid with multi-layer structure is designed to introduce richer information, feature maps output from different substructures of the feature pyramid are fused to provide targeted information for head and overall detection. Secondly, two branches are designed to perform the detection simultaneously. Then, the model generates pedestrian head and overall bounding boxes respectively from predicted centers, heights and offsets thus constituting end-to-end detection. Finally, non-maximum suppression algorithm is improved to make better use of the pedestrian head information. The experimental results show that the proposed algorithm has 50.16% miss rate on CrowdHuman dataset and 10.1% miss rate on the Reasonable subset of CityPersons dataset, and 7.73% miss rate on the Reasonable subset of Caltech dataset. Experimental results show the detection efficiency and generalization performance of the proposed algorithm are improved compared with the contrast algorithms.
The decrease in accuracy of pedestrian detection mainly caused by occlusion and too small scale. Since the pedestrian head is not easily occluded and it’s bounding box contains less background interference, a multi-feature fusion pedestrian detection method combines head and overall information is proposed. Firstly, a feature pyramid with multi-layer structure is designed to introduce richer information, feature maps output from different substructures of the feature pyramid are fused to provide targeted information for head and overall detection. Secondly, two branches are designed to perform the detection simultaneously. Then, the model generates pedestrian head and overall bounding boxes respectively from predicted centers, heights and offsets thus constituting end-to-end detection. Finally, non-maximum suppression algorithm is improved to make better use of the pedestrian head information. The experimental results show that the proposed algorithm has 50.16% miss rate on CrowdHuman dataset and 10.1% miss rate on the Reasonable subset of CityPersons dataset, and 7.73% miss rate on the Reasonable subset of Caltech dataset. Experimental results show the detection efficiency and generalization performance of the proposed algorithm are improved compared with the contrast algorithms.
2022, 44(4): 1461-1468.
doi: 10.11999/JEIT200908
Abstract:
Currently, an effective stream adaptation method for stereo panoramic video transmission is missing. However, the traditional panoramic video adaptive streaming strategy for transmitting binocular stereo panoramic video suffers from the problem of doubling the transmission data and requiring huge bandwidth. A multi-agent reinforcement learning based stereo panoramic video asymmetric transmission adaptive streaming method is proposed in this paper to cope with the limited bandwidth and fluctuation of network bandwidth in real time. First, due to the human eye's preference for the saliency regions of video, each tile in the left and right viewpoints of stereoscopic video contributes differently to the perceptual quality, and a tiles-based method for predicting the watching probability of left and right viewpoint is proposed. Second, a multi-agent reinforcement learning framework based on policy-value (Actor-Critic) is designed for joint rate control of left and right viewpoints. Finally, a reasonable reward function is designed based on the model structure and the principle of binocular suppression. The experimental results show that the proposed method is more suitable for tiles-based stereo panoramic video transmission than the traditional self-adaptive stream transmission strategy. A novel approach is proposed for stereo panoramic video joint rate control and user Quality of Experience (QoE) improvement under limited bandwidth.
Currently, an effective stream adaptation method for stereo panoramic video transmission is missing. However, the traditional panoramic video adaptive streaming strategy for transmitting binocular stereo panoramic video suffers from the problem of doubling the transmission data and requiring huge bandwidth. A multi-agent reinforcement learning based stereo panoramic video asymmetric transmission adaptive streaming method is proposed in this paper to cope with the limited bandwidth and fluctuation of network bandwidth in real time. First, due to the human eye's preference for the saliency regions of video, each tile in the left and right viewpoints of stereoscopic video contributes differently to the perceptual quality, and a tiles-based method for predicting the watching probability of left and right viewpoint is proposed. Second, a multi-agent reinforcement learning framework based on policy-value (Actor-Critic) is designed for joint rate control of left and right viewpoints. Finally, a reasonable reward function is designed based on the model structure and the principle of binocular suppression. The experimental results show that the proposed method is more suitable for tiles-based stereo panoramic video transmission than the traditional self-adaptive stream transmission strategy. A novel approach is proposed for stereo panoramic video joint rate control and user Quality of Experience (QoE) improvement under limited bandwidth.
2022, 44(4): 1469-1475.
doi: 10.11999/JEIT200982
Abstract:
Traditional Two-Dimensional (2D) palmprint recognition is susceptible to the effects of dry humidity, residual image and pressure during image acquisition, which reduces its robustness and accuracy. To solve these problems, Three-Dimensional (3D) palmprint recognition technology is widely studied. The existing 3D palmprint identity authentication technology needs to separate palmprint feature extraction and matching recognition, which not only delays the recognition time, but also increases the difficulty of optimizing the combination of different methods. A 3D palmprint recognition method is proposed based on the fusion of Surface Type (ST) and deep learning. ST images is used to represent 3D palmprint features and to be as input of Convolutional Neural Network (CNN) to realize training. The test image can be automatically extracted the feature information of the palmprint image and complete the identification directly. The experimental results show that the proposed method has an accuracy of 99.43% and a recognition time of 28 ms on the public data set, which has high performance of accuracy and speed compared with the traditional 3D palmprint recognition methods.
Traditional Two-Dimensional (2D) palmprint recognition is susceptible to the effects of dry humidity, residual image and pressure during image acquisition, which reduces its robustness and accuracy. To solve these problems, Three-Dimensional (3D) palmprint recognition technology is widely studied. The existing 3D palmprint identity authentication technology needs to separate palmprint feature extraction and matching recognition, which not only delays the recognition time, but also increases the difficulty of optimizing the combination of different methods. A 3D palmprint recognition method is proposed based on the fusion of Surface Type (ST) and deep learning. ST images is used to represent 3D palmprint features and to be as input of Convolutional Neural Network (CNN) to realize training. The test image can be automatically extracted the feature information of the palmprint image and complete the identification directly. The experimental results show that the proposed method has an accuracy of 99.43% and a recognition time of 28 ms on the public data set, which has high performance of accuracy and speed compared with the traditional 3D palmprint recognition methods.
2022, 44(4): 1476-1483.
doi: 10.11999/JEIT210054
Abstract:
In the commercial process of mobile phone screens, the quality of defect detection affects directly the qualified rate of mobile phone screens. A few defect samples are not enough to complete the training of data-driven segmentation networks, so how to use a few defect samples to complete the defect segmentation is a key problem. In view of this problem, a Co-Attention Segmentation Network (Co-ASNet) is proposed. This network uses Criss-cross attention blocks to capture contextual defect feature information during feature extraction. At the same time, the Co-attention method is applied to enhance the defect feature information interaction between the same defect target in the support image and query image, and then the defect feature representation is reinforced. Also, the improved joint loss function is used to complete the network training. The experimental results show that Co-ASNet can use a few defect samples to achieve an excellent effect of defect segmentation.
In the commercial process of mobile phone screens, the quality of defect detection affects directly the qualified rate of mobile phone screens. A few defect samples are not enough to complete the training of data-driven segmentation networks, so how to use a few defect samples to complete the defect segmentation is a key problem. In view of this problem, a Co-Attention Segmentation Network (Co-ASNet) is proposed. This network uses Criss-cross attention blocks to capture contextual defect feature information during feature extraction. At the same time, the Co-attention method is applied to enhance the defect feature information interaction between the same defect target in the support image and query image, and then the defect feature representation is reinforced. Also, the improved joint loss function is used to complete the network training. The experimental results show that Co-ASNet can use a few defect samples to achieve an excellent effect of defect segmentation.
2022, 44(4): 1484-1494.
doi: 10.11999/JEIT210173
Abstract:
To overcome the problem of easily falling into local extreme values of the whale swarm algorithm when it solves the material emergency scheduling problem with time windows in multiple distribution centers, an Improved Discrete Whale Swarm Algorithm (IDWSA) is proposed. First, a hybrid initialization strategy is used to improve the quality of the initial population. Then two moving rules with similar distribution order and the same distribution center are constructed as comparison items, and an adaptive Cauchy mutation operator and path selection strategy are designed to move individuals. Finally, a global evaluation function is constructed to select individuals to maintain population diversity. On the Solomon standard test set, the distance of the best solution obtained by IDWSA is reduced by 2.25%,13.4%,6% and 1.46% compared with MAPSO,GA,HACO and ABC, respectively, which shortens effectively the driving distance of the vehicle.
To overcome the problem of easily falling into local extreme values of the whale swarm algorithm when it solves the material emergency scheduling problem with time windows in multiple distribution centers, an Improved Discrete Whale Swarm Algorithm (IDWSA) is proposed. First, a hybrid initialization strategy is used to improve the quality of the initial population. Then two moving rules with similar distribution order and the same distribution center are constructed as comparison items, and an adaptive Cauchy mutation operator and path selection strategy are designed to move individuals. Finally, a global evaluation function is constructed to select individuals to maintain population diversity. On the Solomon standard test set, the distance of the best solution obtained by IDWSA is reduced by 2.25%,13.4%,6% and 1.46% compared with MAPSO,GA,HACO and ABC, respectively, which shortens effectively the driving distance of the vehicle.
2022, 44(4): 1495-1502.
doi: 10.11999/JEIT210159
Abstract:
Image composition is an important operation in image processing, but the inharmonious appearance between the foreground region and background makes the composite image look unrealistic. Image harmonization is a very important step in image compositing, and targets at adjusting the appearances of foreground to make it consistent with background, improving the visual quality of output image. However, previous approaches only consider the appearance difference between the foreground and the background of the composite image, and neglect the local brightness change of the image, making the illumination of the whole image inharmonious. In order to solve the problem, in this work, a novel module named Multi-scale Feature Calibration Module (MFCM) is proposed to learn the subtle feature differences between multiple scales of receptive field. Based on the proposed MFCM, a novel encoder is designed further to learn the illumination and brightness change in composite image, followed by a decoder is used to reconstruct image. The foreground normalized regression loss is utilized to instruct the network to learn and adjust the appearances of the foreground. The proposed method is validated on a widely used iHarmony4 dataset. The results show that the proposed method achieves the state of the art and demonstrate the effectiveness of the proposed method.
Image composition is an important operation in image processing, but the inharmonious appearance between the foreground region and background makes the composite image look unrealistic. Image harmonization is a very important step in image compositing, and targets at adjusting the appearances of foreground to make it consistent with background, improving the visual quality of output image. However, previous approaches only consider the appearance difference between the foreground and the background of the composite image, and neglect the local brightness change of the image, making the illumination of the whole image inharmonious. In order to solve the problem, in this work, a novel module named Multi-scale Feature Calibration Module (MFCM) is proposed to learn the subtle feature differences between multiple scales of receptive field. Based on the proposed MFCM, a novel encoder is designed further to learn the illumination and brightness change in composite image, followed by a decoder is used to reconstruct image. The foreground normalized regression loss is utilized to instruct the network to learn and adjust the appearances of the foreground. The proposed method is validated on a widely used iHarmony4 dataset. The results show that the proposed method achieves the state of the art and demonstrate the effectiveness of the proposed method.
2022, 44(4): 1503-1512.
doi: 10.11999/JEIT210059
Abstract:
Field Programmable Gate Array (FPGA) is widely used in Convolutional Neural Network (CNN) hardware acceleration. For better performance, a three-dimensional transformable CNN acceleration structure is proposed by Qu et al (2021). However, this structure brings an explosive growth of the parallelism strategy exploration space, thus the time cost to search the optimal parallelism has surged, which reduces severely the feasibility of accelerator implementation. To solve this issue, a fine-grained iterative optimization parallelism search algorithm is proposed in this paper. The algorithm uses multiple rounds of iterative data filtering to eliminate efficiently the redundant parallelism schemes, compressing more than 99% of the search space. At the same time, the algorithm uses pruning operation to delete invalid calculation branches, and reduces successfully the calculation time from 106 h to less than 10 s. The algorithm can achieve outstanding performance in different kinds of FPGAs, with an average computing resource utilization (R1, R2) up to (0.957, 0.962).
Field Programmable Gate Array (FPGA) is widely used in Convolutional Neural Network (CNN) hardware acceleration. For better performance, a three-dimensional transformable CNN acceleration structure is proposed by Qu et al (2021). However, this structure brings an explosive growth of the parallelism strategy exploration space, thus the time cost to search the optimal parallelism has surged, which reduces severely the feasibility of accelerator implementation. To solve this issue, a fine-grained iterative optimization parallelism search algorithm is proposed in this paper. The algorithm uses multiple rounds of iterative data filtering to eliminate efficiently the redundant parallelism schemes, compressing more than 99% of the search space. At the same time, the algorithm uses pruning operation to delete invalid calculation branches, and reduces successfully the calculation time from 106 h to less than 10 s. The algorithm can achieve outstanding performance in different kinds of FPGAs, with an average computing resource utilization (R1, R2) up to (0.957, 0.962).
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