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2018 Vol. 40, No. 4
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2018, 40(4): 763-769.
doi: 10.11999/JEIT170641
Abstract:
With the introduction of Network Function Virtualization (NFV), the network functions of operator networks are no longer dependent on dedicated hardware devices, and service capabilities of carrier network are more resilient than ever. For more efficient use of resources in NFV-based operator networks, this paper proposes a construction policy of Network Service Chain (NSC). This paper aims to maximize the number of flows that the carrier network can accommodate, a mathematical model is established for NSC construction from the point of view of reducing resource fragmentation due to the unreasonable use of computing resources and network resources. A new greedy NSC construction strategy is designed, which combines the path selection and multiple VNFs deployment of NSC. Numerical simulation result shows that the proposed policy can accommodate more flows and achieve lower end-to-end data latency than the typical policies in the case of the same amount of resources, which improves effectively resource utilization of the general server and switch in operator network.
With the introduction of Network Function Virtualization (NFV), the network functions of operator networks are no longer dependent on dedicated hardware devices, and service capabilities of carrier network are more resilient than ever. For more efficient use of resources in NFV-based operator networks, this paper proposes a construction policy of Network Service Chain (NSC). This paper aims to maximize the number of flows that the carrier network can accommodate, a mathematical model is established for NSC construction from the point of view of reducing resource fragmentation due to the unreasonable use of computing resources and network resources. A new greedy NSC construction strategy is designed, which combines the path selection and multiple VNFs deployment of NSC. Numerical simulation result shows that the proposed policy can accommodate more flows and achieve lower end-to-end data latency than the typical policies in the case of the same amount of resources, which improves effectively resource utilization of the general server and switch in operator network.
2018, 40(4): 770-777.
doi: 10.11999/JEIT170635
Abstract:
Taking into account the energy optimization and performance enhancement of the Content Centric Networking (CCN) comprehensively, an energy optimized implicit collaborative caching scheme for CCN is proposed. In terms of the caching decision, energy saving account is utilized as the judgement, which is carried out on consumer's remote nodes preferentially, and the data packet is utilized to carry the information of recent upstream caching hops, so as to realize the implicit collaboration, thus reducing the caching space competition pressure of the consumers near nodes, improving the caching difference between nearby nodes. As for the caching replacement, the caching content with the minimum energy saving account is selected to be replaced, achieving the optimal energy consumption optimization effect. Simulation results show that, the caching scheme achieves better cache hit ratio and average routing hops, meanwhile, it reduces the network energy consumption effectively.
Taking into account the energy optimization and performance enhancement of the Content Centric Networking (CCN) comprehensively, an energy optimized implicit collaborative caching scheme for CCN is proposed. In terms of the caching decision, energy saving account is utilized as the judgement, which is carried out on consumer's remote nodes preferentially, and the data packet is utilized to carry the information of recent upstream caching hops, so as to realize the implicit collaboration, thus reducing the caching space competition pressure of the consumers near nodes, improving the caching difference between nearby nodes. As for the caching replacement, the caching content with the minimum energy saving account is selected to be replaced, achieving the optimal energy consumption optimization effect. Simulation results show that, the caching scheme achieves better cache hit ratio and average routing hops, meanwhile, it reduces the network energy consumption effectively.
2018, 40(4): 778-784.
doi: 10.11999/JEIT170616
Abstract:
Access control of heterogeneous wireless networks contains many optimization objectives. The optimization objectives of existing algorithms are incompletem and most of them are converted to single objective which restricts the relative relation of each target and can not meet the different demands. Therefore,an access control algorithm is proposed, which uses the multi-objective evolutionary algorithm directly. First, the optimization objective is extended to three, which are the minimization of blocking rate, the minimization of occupancy resources and load balancing. Secondly, the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is introduced and evolutionary strategy is designed to perform preliminary optimization. Finally, the Pareto optimal solution set is obtained by non-dominated sorting, that is, the best access control scheme. The simulation results show that the proposed algorithm improves the accuracy of each optimization objective, and thus improves the success rate of access and the utilization ratio of network resources. It can also provide a variety of optimal access control schemes for decision makers, which can be optimally chosen according to actual requirements.
Access control of heterogeneous wireless networks contains many optimization objectives. The optimization objectives of existing algorithms are incompletem and most of them are converted to single objective which restricts the relative relation of each target and can not meet the different demands. Therefore,an access control algorithm is proposed, which uses the multi-objective evolutionary algorithm directly. First, the optimization objective is extended to three, which are the minimization of blocking rate, the minimization of occupancy resources and load balancing. Secondly, the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) is introduced and evolutionary strategy is designed to perform preliminary optimization. Finally, the Pareto optimal solution set is obtained by non-dominated sorting, that is, the best access control scheme. The simulation results show that the proposed algorithm improves the accuracy of each optimization objective, and thus improves the success rate of access and the utilization ratio of network resources. It can also provide a variety of optimal access control schemes for decision makers, which can be optimally chosen according to actual requirements.
2018, 40(4): 785-793.
doi: 10.11999/JEIT170600
Abstract:
There are many service paths which can satisfy the business requirements in polymorphic routing model of reconfigurable networks. For the issue of the best service path selection, this paper proposes a distributed service path selection algorithm under central control. Each node generates routing tables based on the first function and destination node in service request. The controller monitors the network in real time, regulates paths with high costs and balances the bandwidths and loads of the network. Performance analysis and simulation results show that, the distributed routing tables can generate efficient service paths and the convergence time is proportional to the number of functions. When the proportion of central control is 30 percent, the algorithm has a good performance on average cost of paths and load balance. The response delay to service request decreases almost 50 percent compared with other algorithms.
There are many service paths which can satisfy the business requirements in polymorphic routing model of reconfigurable networks. For the issue of the best service path selection, this paper proposes a distributed service path selection algorithm under central control. Each node generates routing tables based on the first function and destination node in service request. The controller monitors the network in real time, regulates paths with high costs and balances the bandwidths and loads of the network. Performance analysis and simulation results show that, the distributed routing tables can generate efficient service paths and the convergence time is proportional to the number of functions. When the proportion of central control is 30 percent, the algorithm has a good performance on average cost of paths and load balance. The response delay to service request decreases almost 50 percent compared with other algorithms.
2018, 40(4): 794-801.
doi: 10.11999/JEIT170105
Abstract:
Network address shuffling invalidates the address information collected by the attacker with dynamically changing or remapping the hosts network addresses, however, the defense performance of network address shuffling decreases when against scanning attacks which launch attacks at the same time of discovering targets, and few studies analyze theoretically different defense advantages of network address shuffling against scanning attacks of different scanning strategies. In this paper, two strategies of network address shuffling are considered: uniform shuffling and non-repeat shuffling. It presents probabilistic models of scanning attacks in the static address and network address shuffling environments, which analyzes both the probability of the attacker hitting at least one host and the number of hosts hit by the attacker. Then, the defense advantages of both network address shuffling strategies are theoretically calculated and compared with the static address environment. Analysis results indicate that both shuffling strategies have no defense advantages against repeatable scanning attack compared with the static address environment; uniform shuffling has probability advantage against non-repeat scanning attack only when the hosts number is small, and non-repeat shuffling has significant ratio advantage only when the hosts number accounts for a small proportion in the network space size.
Network address shuffling invalidates the address information collected by the attacker with dynamically changing or remapping the hosts network addresses, however, the defense performance of network address shuffling decreases when against scanning attacks which launch attacks at the same time of discovering targets, and few studies analyze theoretically different defense advantages of network address shuffling against scanning attacks of different scanning strategies. In this paper, two strategies of network address shuffling are considered: uniform shuffling and non-repeat shuffling. It presents probabilistic models of scanning attacks in the static address and network address shuffling environments, which analyzes both the probability of the attacker hitting at least one host and the number of hosts hit by the attacker. Then, the defense advantages of both network address shuffling strategies are theoretically calculated and compared with the static address environment. Analysis results indicate that both shuffling strategies have no defense advantages against repeatable scanning attack compared with the static address environment; uniform shuffling has probability advantage against non-repeat scanning attack only when the hosts number is small, and non-repeat shuffling has significant ratio advantage only when the hosts number accounts for a small proportion in the network space size.
2018, 40(4): 802-810.
doi: 10.11999/JEIT170198
Abstract:
This paper puts forward a traceable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme for Monotone Access Structure (MAS), which is proved secure adaptively in the standard model by using composite order bilinear groups. To date, for all traceable CP-ABE schemes, the MAS is represented by the Linear Secret Sharing Scheme (LSSS) and then the data are encrypted by using the corresponding LSSS matrix. Therefore, their encryption costs are linear with the size of the LSSS matrix, and the decryption costs are linear with the number of qualified rows in the LSSS matrix. However, in the proposed traceable CP-ABE scheme, the MAS is represented by the set of minimal authorized set and then the data are encrypted by using the corresponding set. Therefore, the encryption costs are polynomial with the number of minimal authorized set, and for some access policies, the proposed scheme may have shorter ciphertext and lower encryption costs. In addition, the most important thing is that the proposed decryption needs only three bilinear pairing computations and two exponent computations, which improves the efficiency extremely. Finally, the full security proof of the proposed scheme is given by using three static assumptions along with the detailed performance analysis and experiment validation.
This paper puts forward a traceable Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme for Monotone Access Structure (MAS), which is proved secure adaptively in the standard model by using composite order bilinear groups. To date, for all traceable CP-ABE schemes, the MAS is represented by the Linear Secret Sharing Scheme (LSSS) and then the data are encrypted by using the corresponding LSSS matrix. Therefore, their encryption costs are linear with the size of the LSSS matrix, and the decryption costs are linear with the number of qualified rows in the LSSS matrix. However, in the proposed traceable CP-ABE scheme, the MAS is represented by the set of minimal authorized set and then the data are encrypted by using the corresponding set. Therefore, the encryption costs are polynomial with the number of minimal authorized set, and for some access policies, the proposed scheme may have shorter ciphertext and lower encryption costs. In addition, the most important thing is that the proposed decryption needs only three bilinear pairing computations and two exponent computations, which improves the efficiency extremely. Finally, the full security proof of the proposed scheme is given by using three static assumptions along with the detailed performance analysis and experiment validation.
2018, 40(4): 811-817.
doi: 10.11999/JEIT170628
Abstract:
To resolve the problem of poor security on quantum attack in attribute-based encryption over the bilinear maps, a new multi-authority attribute-based encryption scheme on lattices is proposed. Firstly, the SamepleLeft algorithm was used to extract the users private key, so it can reduce the size of private key which is related to the dimensions of concatenation matrix and the quantity of users attributes. Secondly, aiming at the problem of single access policy, the new scheme employed the Shamir secret sharing scheme which is used to construct an access tree to realized AND, OR, THRESHOLD operations on attributes, so the ciphertext can be generated by any access structure, and the expressive skill of access policy is more extensive. Lastly, the security of the proposed scheme could reduce to the hardness of decisional learning with errors problem under standard model. Comparative analysis shows that, the size of public parameters, master secret key, users private key and ciphertext are all optimized, and it has better performance than single authority schemes in some degree. Furthermore, from perspective of security and practicability, the new multi-authority attribute-based encryption scheme is more suitable for the demand of cloud environment.
To resolve the problem of poor security on quantum attack in attribute-based encryption over the bilinear maps, a new multi-authority attribute-based encryption scheme on lattices is proposed. Firstly, the SamepleLeft algorithm was used to extract the users private key, so it can reduce the size of private key which is related to the dimensions of concatenation matrix and the quantity of users attributes. Secondly, aiming at the problem of single access policy, the new scheme employed the Shamir secret sharing scheme which is used to construct an access tree to realized AND, OR, THRESHOLD operations on attributes, so the ciphertext can be generated by any access structure, and the expressive skill of access policy is more extensive. Lastly, the security of the proposed scheme could reduce to the hardness of decisional learning with errors problem under standard model. Comparative analysis shows that, the size of public parameters, master secret key, users private key and ciphertext are all optimized, and it has better performance than single authority schemes in some degree. Furthermore, from perspective of security and practicability, the new multi-authority attribute-based encryption scheme is more suitable for the demand of cloud environment.
2018, 40(4): 818-824.
doi: 10.11999/JEIT170613
Abstract:
In allusion to the low efficiency of traditional spectrum access strategies caused by high spectrum sensing uncertainty of satellite cognitive communication network, a novel optimized channel access strategy based on dynamic multi-spectrum sensing is proposed. The cognitive LEO satellite could adjust the probability of channel access in time with different spectrum sensing results according to the relationship between spectrum detection probability and maximum interference threshold of primary user. In order to achieve the maximize throughput of cognitive system, a strategy for decision threshold selection based on joint optimization between spectrum detection probability and false alarm probability is designed, and the optimized number of sensing spectrums is derived. Simulation results indicate that cognitive LEO satellite is able to select optimized spectrum sensing strategy dynamically according to the idle states of primary channels with the interference limitation of primary user, and access primary channels more positively especially when the signal-to-noise ratio of detecting signals is low. The influence of spectrum sensing uncertainty on the efficiency of channel access is depressed and the throughput of cognitive system is improved by the proposed channel access strategy.
In allusion to the low efficiency of traditional spectrum access strategies caused by high spectrum sensing uncertainty of satellite cognitive communication network, a novel optimized channel access strategy based on dynamic multi-spectrum sensing is proposed. The cognitive LEO satellite could adjust the probability of channel access in time with different spectrum sensing results according to the relationship between spectrum detection probability and maximum interference threshold of primary user. In order to achieve the maximize throughput of cognitive system, a strategy for decision threshold selection based on joint optimization between spectrum detection probability and false alarm probability is designed, and the optimized number of sensing spectrums is derived. Simulation results indicate that cognitive LEO satellite is able to select optimized spectrum sensing strategy dynamically according to the idle states of primary channels with the interference limitation of primary user, and access primary channels more positively especially when the signal-to-noise ratio of detecting signals is low. The influence of spectrum sensing uncertainty on the efficiency of channel access is depressed and the throughput of cognitive system is improved by the proposed channel access strategy.
2018, 40(4): 825-831.
doi: 10.11999/JEIT170673
Abstract:
In view of the fact that there exists little research on the constellation characteristic of Multiple Parameters Weighted-type FRactional Fourier Transform (MP-WFRFT), a novel mathematical model based on constellation superposition is proposed. By analyzing the time domain and frequency domain components of the MP-WFRFT modulated signal, the quantitative and qualitative conclusions are provided, about the constellation splitting and Gaussian-like characteristics and the constellation precoding system is built based on MP-WFRFT. The related mixed integer optimization model is put forward and the Genetic Algorithm (GA) is used to solute the optimization, Then the desired MP-WFRFT-based precoding system is obtained. The simulation results demonstrate that the proposed constellation superposition model and its corresponding optimization model are correct. Besides, the tests obtained on USRP platforms also show that the constellation precoding system based on MP-WFRFT is practical.
In view of the fact that there exists little research on the constellation characteristic of Multiple Parameters Weighted-type FRactional Fourier Transform (MP-WFRFT), a novel mathematical model based on constellation superposition is proposed. By analyzing the time domain and frequency domain components of the MP-WFRFT modulated signal, the quantitative and qualitative conclusions are provided, about the constellation splitting and Gaussian-like characteristics and the constellation precoding system is built based on MP-WFRFT. The related mixed integer optimization model is put forward and the Genetic Algorithm (GA) is used to solute the optimization, Then the desired MP-WFRFT-based precoding system is obtained. The simulation results demonstrate that the proposed constellation superposition model and its corresponding optimization model are correct. Besides, the tests obtained on USRP platforms also show that the constellation precoding system based on MP-WFRFT is practical.
2018, 40(4): 832-838.
doi: 10.11999/JEIT170618
Abstract:
Multi-User Shared Access (MUSA) is a non-orthogonal multiple access technology, and its performance will be affected by the spreading sequence due to the usage of complex domain spread spectrum technology, so the spreading sequences should be optimized and improved. Considering the problem that only the influence of cross-correlation peak is taken into account in the process of cross-correlation optimization in the existing optimization algorithm, an improved spreading sequence optimization algorithm is proposed. Both the influences of cross-correlation peak and the cross-correlation mean square are taken into consideration. The simulation results show that the proposed algorithm can promote 0.9 dB SNR performance in the same BER than the existing algorithm. In addition, the random selection of the spreading sequences will lead to the collision of the sequence. In order to reduce the collision probability, a set of improved complex spreading sequences based on constellation figure of merit is proposed. The proposed complex sequence has a higher constellation figure of merit than the triple-level complex spreading sequence. Additionally, more excellent spreading sequences can be obtained after optimization, and when compared with the optimized triple-level complex spreading sequence, the SNR performance can promote about 1 dB in the same BER and the user overloading ratio can improve 15% when BER is 10-13, which can further promote the performance of MUSA system.
Multi-User Shared Access (MUSA) is a non-orthogonal multiple access technology, and its performance will be affected by the spreading sequence due to the usage of complex domain spread spectrum technology, so the spreading sequences should be optimized and improved. Considering the problem that only the influence of cross-correlation peak is taken into account in the process of cross-correlation optimization in the existing optimization algorithm, an improved spreading sequence optimization algorithm is proposed. Both the influences of cross-correlation peak and the cross-correlation mean square are taken into consideration. The simulation results show that the proposed algorithm can promote 0.9 dB SNR performance in the same BER than the existing algorithm. In addition, the random selection of the spreading sequences will lead to the collision of the sequence. In order to reduce the collision probability, a set of improved complex spreading sequences based on constellation figure of merit is proposed. The proposed complex sequence has a higher constellation figure of merit than the triple-level complex spreading sequence. Additionally, more excellent spreading sequences can be obtained after optimization, and when compared with the optimized triple-level complex spreading sequence, the SNR performance can promote about 1 dB in the same BER and the user overloading ratio can improve 15% when BER is 10-13, which can further promote the performance of MUSA system.
2018, 40(4): 839-845.
doi: 10.11999/JEIT170605
Abstract:
Considering the blind recognition of (n,1,m) convolutional codes at high bit error rate, a novel method based on modified Walsh-Hadamard Transform (WHT) is presented. First, the original issue is equivalent to the blind recognition of several 1/2 rate convolutional codes, and a system of linear equations for generating polynomial coefficients is established. Disadvantages of the existing methods based on WHT are analyzed, after which a more robust decision threshold is deduced, with a reduction in computational complexity by limiting the range of roots, and then the code length is recognized while the correct solution vector is found. Finally, the generator polynomial matrix of (n,1,m) convolutional codes is obtained by combining the generator polynomial of the equivalent 1/2 rate convolutional codes. The simulation results verify the effectiveness of the proposed method, which has a better performance when comparing to the traditional method.
Considering the blind recognition of (n,1,m) convolutional codes at high bit error rate, a novel method based on modified Walsh-Hadamard Transform (WHT) is presented. First, the original issue is equivalent to the blind recognition of several 1/2 rate convolutional codes, and a system of linear equations for generating polynomial coefficients is established. Disadvantages of the existing methods based on WHT are analyzed, after which a more robust decision threshold is deduced, with a reduction in computational complexity by limiting the range of roots, and then the code length is recognized while the correct solution vector is found. Finally, the generator polynomial matrix of (n,1,m) convolutional codes is obtained by combining the generator polynomial of the equivalent 1/2 rate convolutional codes. The simulation results verify the effectiveness of the proposed method, which has a better performance when comparing to the traditional method.
2018, 40(4): 846-852.
doi: 10.11999/JEIT170319
Abstract:
In view of the excellent signal estimation performance of the Kalman Filter (KF), combining the KF algorithm with the greedy algorithm and an imaging method is presented for Inverse Synthetic Aperture Radar (ISAR) using KF with sparse constraints. Large space targets including the targets having large-size components and long imaging time may introduce the Migration Through Resolution Cell (MTRC) and quadratic phase modulation in the cross-range. The MTRC correction is firstly performed. Then, the observation matrix is constructed by including the quadratic phase term. By maximizing the image sharpness, an estimation of the target angular velocity as well as a well-focused image can be obtained. The estimated angular velocity can be further used for image cross-range scaling. The processing of the simulated satellite ISAR data verifies the effectiveness of the presented imaging processing method. The image quality is superior to the traditional Range Doppler (RD) method and Orthogonal Matching Pursuit (OMP) method.
In view of the excellent signal estimation performance of the Kalman Filter (KF), combining the KF algorithm with the greedy algorithm and an imaging method is presented for Inverse Synthetic Aperture Radar (ISAR) using KF with sparse constraints. Large space targets including the targets having large-size components and long imaging time may introduce the Migration Through Resolution Cell (MTRC) and quadratic phase modulation in the cross-range. The MTRC correction is firstly performed. Then, the observation matrix is constructed by including the quadratic phase term. By maximizing the image sharpness, an estimation of the target angular velocity as well as a well-focused image can be obtained. The estimated angular velocity can be further used for image cross-range scaling. The processing of the simulated satellite ISAR data verifies the effectiveness of the presented imaging processing method. The image quality is superior to the traditional Range Doppler (RD) method and Orthogonal Matching Pursuit (OMP) method.
2018, 40(4): 853-859.
doi: 10.11999/JEIT170719
Abstract:
In through-wall radar building layout imaging, the existing extended target sparse imaging method can not effectively exploit the structural sparsity of the wall reflections in the scene, resulting in incoherent imaging and unobvious contour of walls. A sparse Bayesian learning method is proposed for building layout imaging by exploiting the inter-block coupling of sparse signal. On the basis of the hierarchical Gaussian prior model of block sparse signal characteristics, the inter-block coupling coefficient is further used to characterize the structured sparsity of the wall reflections. Then these coefficients are introduced into the hyperparameters controlling the prior distribution of sparse signal, thus this structured sparsity is transformed into the coupling relationship of these hyperparameters. Susequently, an Expectation-Maximization (EM) algorithm is developed to infer the Maximum A Posterior (MAP) estimate of these hyperparameters. The results of simulation and experiment show that the proposed method improves effectively the imaging quality of the building wall.
In through-wall radar building layout imaging, the existing extended target sparse imaging method can not effectively exploit the structural sparsity of the wall reflections in the scene, resulting in incoherent imaging and unobvious contour of walls. A sparse Bayesian learning method is proposed for building layout imaging by exploiting the inter-block coupling of sparse signal. On the basis of the hierarchical Gaussian prior model of block sparse signal characteristics, the inter-block coupling coefficient is further used to characterize the structured sparsity of the wall reflections. Then these coefficients are introduced into the hyperparameters controlling the prior distribution of sparse signal, thus this structured sparsity is transformed into the coupling relationship of these hyperparameters. Susequently, an Expectation-Maximization (EM) algorithm is developed to infer the Maximum A Posterior (MAP) estimate of these hyperparameters. The results of simulation and experiment show that the proposed method improves effectively the imaging quality of the building wall.
2018, 40(4): 860-866.
doi: 10.11999/JEIT170630
Abstract:
A new azimuth imaging algorithm based on the terahertz single frequency MIMO arc array is proposed in this paper. The MIMO arc array is transformed to the MIMO linear array equivalently, based on which the arc array azimuth image reconstruction is designed and realized by using the RMA algorithm. This paper describes the equivalent transform between the MIMO arc array and the MIMO linear array in detail, constructs the imaging signal model, designs and presents the terahertz single frequency azimuth imaging algorithm based on the MIMO arc array. Finally, the feasibility and effectiveness of the algorithm is verified by computer simulation.
A new azimuth imaging algorithm based on the terahertz single frequency MIMO arc array is proposed in this paper. The MIMO arc array is transformed to the MIMO linear array equivalently, based on which the arc array azimuth image reconstruction is designed and realized by using the RMA algorithm. This paper describes the equivalent transform between the MIMO arc array and the MIMO linear array in detail, constructs the imaging signal model, designs and presents the terahertz single frequency azimuth imaging algorithm based on the MIMO arc array. Finally, the feasibility and effectiveness of the algorithm is verified by computer simulation.
2018, 40(4): 867-874.
doi: 10.11999/JEIT170612
Abstract:
To solve the cross-channel signal problem when receiving wideband radar signals with the conventional wideband digital receiver, and the blind intra-pulse modulation recognition problem for Low Probability of Intercept (LPI) radar signals, a new wideband digital receiver based on the Modulated Wideband Converter (MWC) discrete compressed sampling structure is proposed to intercept and recognize the wideband radar signals. The proposed structure uses the pseudo-random sequences to mix the received signals to baseband and other sub-bands, the mixed signals are then low-pass filtered and down-sampled to get the baseband compressed sampling data, which could solve the cross-channel signal problem flexibly. Furthermore, a recognition method based on the Short-Time Fourier Transform (STFT) and the spectrum energy focusing rate test is proposed. Firstly, the STFT spectrum bandwidth is tested to distinguish phase modulation signals and frequency modulation signals recognition roughly. Then, the spectrum energy focusing rate of the compressed sampling data is tested to recognize the intra-pulse modulation type specifically. Finally, simulation results validate the efficiencies of the proposed receiver and the proposed recognition method in low Signal-to-Noise Rations (SNR).
To solve the cross-channel signal problem when receiving wideband radar signals with the conventional wideband digital receiver, and the blind intra-pulse modulation recognition problem for Low Probability of Intercept (LPI) radar signals, a new wideband digital receiver based on the Modulated Wideband Converter (MWC) discrete compressed sampling structure is proposed to intercept and recognize the wideband radar signals. The proposed structure uses the pseudo-random sequences to mix the received signals to baseband and other sub-bands, the mixed signals are then low-pass filtered and down-sampled to get the baseband compressed sampling data, which could solve the cross-channel signal problem flexibly. Furthermore, a recognition method based on the Short-Time Fourier Transform (STFT) and the spectrum energy focusing rate test is proposed. Firstly, the STFT spectrum bandwidth is tested to distinguish phase modulation signals and frequency modulation signals recognition roughly. Then, the spectrum energy focusing rate of the compressed sampling data is tested to recognize the intra-pulse modulation type specifically. Finally, simulation results validate the efficiencies of the proposed receiver and the proposed recognition method in low Signal-to-Noise Rations (SNR).
2018, 40(4): 875-881.
doi: 10.11999/JEIT170588
Abstract:
In order to solve the problem that the correct recognition rate of Low Probability of Intercept (LPI) radar signal is low and the feature extraction is difficult, an automatic classification and recognition system based on Choi-Williams Distribution (CWD) and stacked Sparse Auto-Encoder (sSAE) is proposed. The system starts from the time-frequency image which reflects the essential characteristics of the signal. Firstly, the CWD is performed on the LPI radar signal to obtain the two-dimensional time-frequency image. Then, the obtained time-frequency original image is preprocessed and the preprocessed image is sent into the multilayer SAE for off-line training. Finally, the feature automatically extracted from the SAE is sent to the softmax classifier, to achieve on-line classification and identification of the radar signal. Simulation results show that the classification system achieves overall correct recognition rate of 96.4% at SNR of for the eight LPI radar signals (LFM, BPSK, Costas, Frank and T1~T4), which is better than the method of manually designing the extract signal characteristics under low SNR conditions.
In order to solve the problem that the correct recognition rate of Low Probability of Intercept (LPI) radar signal is low and the feature extraction is difficult, an automatic classification and recognition system based on Choi-Williams Distribution (CWD) and stacked Sparse Auto-Encoder (sSAE) is proposed. The system starts from the time-frequency image which reflects the essential characteristics of the signal. Firstly, the CWD is performed on the LPI radar signal to obtain the two-dimensional time-frequency image. Then, the obtained time-frequency original image is preprocessed and the preprocessed image is sent into the multilayer SAE for off-line training. Finally, the feature automatically extracted from the SAE is sent to the softmax classifier, to achieve on-line classification and identification of the radar signal. Simulation results show that the classification system achieves overall correct recognition rate of 96.4% at SNR of for the eight LPI radar signals (LFM, BPSK, Costas, Frank and T1~T4), which is better than the method of manually designing the extract signal characteristics under low SNR conditions.
2018, 40(4): 882-889.
doi: 10.11999/JEIT170677
Abstract:
A geometrical location method based on the R-D principle is proposed. In the case of the location method based on image matching target method will be invalid, when the target area does not have the matching point, such as: sea target, grassland target, etc. First, a geometrical model between the transceiver platform and the target is constructed at the time of center of adjacent synthetic aperture. Then the position information of the target relative of the receiving platform can be solved by combining the relationship between the target in the SAR image and the center point of scene. Finally, according to the characteristic of bistatic forward-looking SAR system, the error models of synchronization errors are built. What is more, through the analysis of the simulation results, the influence of the error on the positioning accuracy is given. Simulation results show the validity of the positioning method and the proposed error models.
A geometrical location method based on the R-D principle is proposed. In the case of the location method based on image matching target method will be invalid, when the target area does not have the matching point, such as: sea target, grassland target, etc. First, a geometrical model between the transceiver platform and the target is constructed at the time of center of adjacent synthetic aperture. Then the position information of the target relative of the receiving platform can be solved by combining the relationship between the target in the SAR image and the center point of scene. Finally, according to the characteristic of bistatic forward-looking SAR system, the error models of synchronization errors are built. What is more, through the analysis of the simulation results, the influence of the error on the positioning accuracy is given. Simulation results show the validity of the positioning method and the proposed error models.
2018, 40(4): 890-897.
doi: 10.11999/JEIT170622
Abstract:
In view of detection and tracking issue with large phased array radar for near-space hypersonic targets, a Modified Triple-Stage Hough Transform (MTS-HT) Track-Before-Detect (TBD) algorithm is proposed. Firstly, in order to reduce huge computational load problem from direct three-dimensional Hough transform, three- dimensional measurement points are mapped into range-time plane, azimuth-time plane and elevation-time plane in turn for two-dimensional Hough transforms. Besides, to decrease the impact from strong interference, meanwhile, make full use of energy information of points, point selection is carried on by double integration means of non-coherent integration and binary integration in every stage. Finally, false trajectory will be reduced effectively by motion constraints as well as merging of similar trajectories and the final trajectory from sequence association can be obtained. Simulation results show that proposed algorithm can realize effective detection and tracking for near-space hypersonic targets with relatively high trajectory detection probability and low false trajectory detection probability.
In view of detection and tracking issue with large phased array radar for near-space hypersonic targets, a Modified Triple-Stage Hough Transform (MTS-HT) Track-Before-Detect (TBD) algorithm is proposed. Firstly, in order to reduce huge computational load problem from direct three-dimensional Hough transform, three- dimensional measurement points are mapped into range-time plane, azimuth-time plane and elevation-time plane in turn for two-dimensional Hough transforms. Besides, to decrease the impact from strong interference, meanwhile, make full use of energy information of points, point selection is carried on by double integration means of non-coherent integration and binary integration in every stage. Finally, false trajectory will be reduced effectively by motion constraints as well as merging of similar trajectories and the final trajectory from sequence association can be obtained. Simulation results show that proposed algorithm can realize effective detection and tracking for near-space hypersonic targets with relatively high trajectory detection probability and low false trajectory detection probability.
2018, 40(4): 898-904.
doi: 10.11999/JEIT170636
Abstract:
Jamming is the one of most common and serious threats for Automatic Dependent Surveillance- Broadcast (ADS-B) system. Minimum dispersion method is applied to jamming suppression for ADS-B in this paper. Firstly, based on the pulse characteristic of ADS-B, minimum dispersion method is obtained by solving the constrained optimization issue of p-norm, which expands the case that exploits only the second-order statistics of the array output in past. Then, the useful information in lower order statistics is used to convert the solution of optimal weight vector into an optimization problem. Finally, jamming is mitigated by spatial filtering and the performance is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm. The anti-jamming performance of this method is not limited by the prior knowledge of ADS-B direction and the covariance matrix estimation.
Jamming is the one of most common and serious threats for Automatic Dependent Surveillance- Broadcast (ADS-B) system. Minimum dispersion method is applied to jamming suppression for ADS-B in this paper. Firstly, based on the pulse characteristic of ADS-B, minimum dispersion method is obtained by solving the constrained optimization issue of p-norm, which expands the case that exploits only the second-order statistics of the array output in past. Then, the useful information in lower order statistics is used to convert the solution of optimal weight vector into an optimization problem. Finally, jamming is mitigated by spatial filtering and the performance is analyzed. Simulation results demonstrate the effectiveness of the proposed algorithm. The anti-jamming performance of this method is not limited by the prior knowledge of ADS-B direction and the covariance matrix estimation.
2018, 40(4): 905-911.
doi: 10.11999/JEIT170440
Abstract:
The quality of Signal-In-Space (SIS) for Global Navigation Satellite System (GNSS) is directly linked to users Positioning, Velocity and Timing (PVT) service. Limited to the insensitivity of authorized signals, the pseudo codes can not be obtained, so it is difficult to assess the authorized signal quality. This paper mainly focus on analyzing the Coherent Adaptive Subcarrier Modulation (CASM) signal on L1 frequency points for GPS BIIF-5 satellite and the code sequences of P(Y) and M signal are resumed by using matched filtering technology. The power distribution of each signal component can be solved through the maximum likelihood estimation theory combined with the signal characteristics. This paper laies special stress on quantitatively analyzing the correlation performance on P(Y) and M signals, including correlation curve, correlation loss and S-curve biases. Based on this complete authorized signals quality assessment method for GPS L1, the research productions can be reference to other satellite navigation systems authorized signals.
The quality of Signal-In-Space (SIS) for Global Navigation Satellite System (GNSS) is directly linked to users Positioning, Velocity and Timing (PVT) service. Limited to the insensitivity of authorized signals, the pseudo codes can not be obtained, so it is difficult to assess the authorized signal quality. This paper mainly focus on analyzing the Coherent Adaptive Subcarrier Modulation (CASM) signal on L1 frequency points for GPS BIIF-5 satellite and the code sequences of P(Y) and M signal are resumed by using matched filtering technology. The power distribution of each signal component can be solved through the maximum likelihood estimation theory combined with the signal characteristics. This paper laies special stress on quantitatively analyzing the correlation performance on P(Y) and M signals, including correlation curve, correlation loss and S-curve biases. Based on this complete authorized signals quality assessment method for GPS L1, the research productions can be reference to other satellite navigation systems authorized signals.
2018, 40(4): 912-919.
doi: 10.11999/JEIT170666
Abstract:
In multi-target tracking algorithms under the Bayesian filtering framework, it is usually assumed that the priori knowledge of clutter is known. However, in practice, the knowledge of clutter is usually unknown, and the assumption of clutter may not agree with the truth, resulting in the filtering precision declining. For this problem, this paper addresses the problem of Cardinalized Probability Hypothesis Density (CPHD) filter with clutter estimation. Firstly, this paper presents a new CPHD filter with clutter estimation based on Dirichlet Process Mixture Model (DPMM). Thus, this DPMM--CPHD algorithm can reduce the estimation error of the clutter spatial distribution effectively by selecting an appropriate class number. Secondly, to solve the clutter overestimation and cardinality underestimation problems, a correction idea of the sample set via CPHD filter recursion is proposed. By introducing this idea to the DPMM--CPHD algorithm, an improved DPMM--CPHD algorithm is proposed to solve this intractability of errors on clutter number and target number. Simulation results show that the proposed algorithm can effectively estimate the unknown parameters of clutter and has a good performance of multi-target tracking.
In multi-target tracking algorithms under the Bayesian filtering framework, it is usually assumed that the priori knowledge of clutter is known. However, in practice, the knowledge of clutter is usually unknown, and the assumption of clutter may not agree with the truth, resulting in the filtering precision declining. For this problem, this paper addresses the problem of Cardinalized Probability Hypothesis Density (CPHD) filter with clutter estimation. Firstly, this paper presents a new CPHD filter with clutter estimation based on Dirichlet Process Mixture Model (DPMM). Thus, this DPMM--CPHD algorithm can reduce the estimation error of the clutter spatial distribution effectively by selecting an appropriate class number. Secondly, to solve the clutter overestimation and cardinality underestimation problems, a correction idea of the sample set via CPHD filter recursion is proposed. By introducing this idea to the DPMM--CPHD algorithm, an improved DPMM--CPHD algorithm is proposed to solve this intractability of errors on clutter number and target number. Simulation results show that the proposed algorithm can effectively estimate the unknown parameters of clutter and has a good performance of multi-target tracking.
2018, 40(4): 920-927.
doi: 10.11999/JEIT170699
Abstract:
To solve the problem that the traditional point set non-rigid registration algorithm has low precision and slow convergence speed for complex local deformation data, this paper proposes a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the non-rigid registration of the point set from coarse to fine. In each iteration, the sub shape point sets and sub target point sets are divided and the shape control points of each sub point set are updated. Then the control point guided affine Iterative Closest Point (ICP) algorithm is used to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Until the registration error converges, the loop ends and outputs the updated shape point set. Experimental results demonstrate that the accuracy and convergence of the proposed algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.
To solve the problem that the traditional point set non-rigid registration algorithm has low precision and slow convergence speed for complex local deformation data, this paper proposes a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the non-rigid registration of the point set from coarse to fine. In each iteration, the sub shape point sets and sub target point sets are divided and the shape control points of each sub point set are updated. Then the control point guided affine Iterative Closest Point (ICP) algorithm is used to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Until the registration error converges, the loop ends and outputs the updated shape point set. Experimental results demonstrate that the accuracy and convergence of the proposed algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.
2018, 40(4): 928-935.
doi: 10.11999/JEIT170703
Abstract:
In order to improve the operation efficiency of the RANdom SAmple Consensus (RANSAC) in feature registration, the Random Sample Consensus based on Feature Distance and Inliers (RSCFDI) is proposed. Firstly, the priori probability guidance method based on feature distance is proposed for increasing the probability of searching correct model in each loop. Then, to increase the convergence rate, the random sampling and calculation method based on sample set and inliers is adopted. Finally, the jumping out loop based on unchanged maximum is proposed to fit the proposed loop breaking, and the execution speed is elevated. The theoretical proof and experimental results show that the RSCFDI ensures the robustness of the algorithm, and the operation efficiency is improved.
In order to improve the operation efficiency of the RANdom SAmple Consensus (RANSAC) in feature registration, the Random Sample Consensus based on Feature Distance and Inliers (RSCFDI) is proposed. Firstly, the priori probability guidance method based on feature distance is proposed for increasing the probability of searching correct model in each loop. Then, to increase the convergence rate, the random sampling and calculation method based on sample set and inliers is adopted. Finally, the jumping out loop based on unchanged maximum is proposed to fit the proposed loop breaking, and the execution speed is elevated. The theoretical proof and experimental results show that the RSCFDI ensures the robustness of the algorithm, and the operation efficiency is improved.
2018, 40(4): 936-943.
doi: 10.11999/JEIT170657
Abstract:
In order to improve the palmprint recognition rate, this paper improves differential excitation and gradient orientation of Weber Local Descriptor (WLD) based on the texture features of palmprint images, and proposes a Double Gabor orientation Weber Local Descriptor (DGWLD). The directional information of the difference between the neighborhood pixels and the central pixel is considered to enlarge the difference between palmprint, when constructing the new differential excitation map. At the same time, gradient orientation is replaced by double Gabor orientation to reduce the influence of translation and rotation. In addition, a feature cross matching algorithm is used for further improve the recognition rate. Experiments on PolyU, MSpalmprint and CASIA palmprint databases show that the recognition rate is up to 100%. The experimental results show that the proposed method is superior in terms of identification rate and equal error rate compared with other local descriptor methods and improved WLD methods.
In order to improve the palmprint recognition rate, this paper improves differential excitation and gradient orientation of Weber Local Descriptor (WLD) based on the texture features of palmprint images, and proposes a Double Gabor orientation Weber Local Descriptor (DGWLD). The directional information of the difference between the neighborhood pixels and the central pixel is considered to enlarge the difference between palmprint, when constructing the new differential excitation map. At the same time, gradient orientation is replaced by double Gabor orientation to reduce the influence of translation and rotation. In addition, a feature cross matching algorithm is used for further improve the recognition rate. Experiments on PolyU, MSpalmprint and CASIA palmprint databases show that the recognition rate is up to 100%. The experimental results show that the proposed method is superior in terms of identification rate and equal error rate compared with other local descriptor methods and improved WLD methods.
2018, 40(4): 944-950.
doi: 10.11999/JEIT170654
Abstract:
In order to solve the problem of low throughput rate and efficiency of the current dynamic frame slot ALOHA collision algorithms, a grouped dynamic frame slotted ALOHA tag anti-collision algorithm based on Parallelizable identification (PIGDFSA) is proposed. Based on the experiments, the method and strategy of increasing the system throughput rate and lowering the tag collision rate are presented by exploring effects of the number of the tags and its groups, the frame length on the system throughout and tag collision rate. Combining the multi-antenna of the RFID system and FastICA technology, the collision slot can be redefined, and the number of the unrecognized tags can be used to set the number of groups and frame length adaptively. The simulation results show that the PIGDFSA algorithm can stabilize the throughput rate more than 92% when the number of tags reaches 2000, and it has higher throughput rate, lesser idle slot and higher algorithm efficiency compared with the FSA-256, GDFSA, and BSDBG algorithm.
In order to solve the problem of low throughput rate and efficiency of the current dynamic frame slot ALOHA collision algorithms, a grouped dynamic frame slotted ALOHA tag anti-collision algorithm based on Parallelizable identification (PIGDFSA) is proposed. Based on the experiments, the method and strategy of increasing the system throughput rate and lowering the tag collision rate are presented by exploring effects of the number of the tags and its groups, the frame length on the system throughout and tag collision rate. Combining the multi-antenna of the RFID system and FastICA technology, the collision slot can be redefined, and the number of the unrecognized tags can be used to set the number of groups and frame length adaptively. The simulation results show that the PIGDFSA algorithm can stabilize the throughput rate more than 92% when the number of tags reaches 2000, and it has higher throughput rate, lesser idle slot and higher algorithm efficiency compared with the FSA-256, GDFSA, and BSDBG algorithm.
2018, 40(4): 951-956.
doi: 10.11999/JEIT170627
Abstract:
As an important milestone for the development of the artificial intelligence, gesture recognition enables the human-computer interaction and has received significantly growing research interest nowadays. However, the current technology for the gesture recognition has the low quality in the gesture rotation, translation and scaling. To solve the problem, a novel network structure named Densenet_V2 is proposed, and it is based on Dense Convolutional Networks (Densenet) and Spatial Transformer Networks (STN). Firstly, the input samples and feature maps are spatially transformed and aligned with the STN. Then the effective features of gestures are automatically extracted by using the Densenet. Finally, the linear classifier is adopted to classify the gestures. To prevent the network model from over-fitting the sample data set, the L2 regular term is involved into the loss function to achieve the weight decay when training the network. Experiments on the Marcel gesture database show that Densenet_V2 can improve the recognition rate of static deformation gestures.
As an important milestone for the development of the artificial intelligence, gesture recognition enables the human-computer interaction and has received significantly growing research interest nowadays. However, the current technology for the gesture recognition has the low quality in the gesture rotation, translation and scaling. To solve the problem, a novel network structure named Densenet_V2 is proposed, and it is based on Dense Convolutional Networks (Densenet) and Spatial Transformer Networks (STN). Firstly, the input samples and feature maps are spatially transformed and aligned with the STN. Then the effective features of gestures are automatically extracted by using the Densenet. Finally, the linear classifier is adopted to classify the gestures. To prevent the network model from over-fitting the sample data set, the L2 regular term is involved into the loss function to achieve the weight decay when training the network. Experiments on the Marcel gesture database show that Densenet_V2 can improve the recognition rate of static deformation gestures.
2018, 40(4): 957-963.
doi: 10.11999/JEIT170623
Abstract:
In the recommendation system, a critical challenge is that individual environment context log may not contain sufficient item access records for mining his/her environment context preferences. This paper designs a Contextual Topic-based Relevance Recommendation (CTRR) algorithm. The CTRR algorithm uses the CTRR_LDA model and a postfiltering strategy to recommend items to users in a specific environment context. CTRR_LDA is an improved LDA model, which combines environment contexts and item feature contexts to calculate the probability of the item appeared. In this model, the environment context is divided into multiple environment context factors. Each environment context factor can be expressed as a K-dimensional topic distribution. Then the CTRR_LDA model is used to mine the latent topic of the items in each environment context factor. According to the experimental results on the LDOS-CoMoDa datasets, the reliability of algorithm is validated in the context-aware recommendation scenario.
In the recommendation system, a critical challenge is that individual environment context log may not contain sufficient item access records for mining his/her environment context preferences. This paper designs a Contextual Topic-based Relevance Recommendation (CTRR) algorithm. The CTRR algorithm uses the CTRR_LDA model and a postfiltering strategy to recommend items to users in a specific environment context. CTRR_LDA is an improved LDA model, which combines environment contexts and item feature contexts to calculate the probability of the item appeared. In this model, the environment context is divided into multiple environment context factors. Each environment context factor can be expressed as a K-dimensional topic distribution. Then the CTRR_LDA model is used to mine the latent topic of the items in each environment context factor. According to the experimental results on the LDOS-CoMoDa datasets, the reliability of algorithm is validated in the context-aware recommendation scenario.
2018, 40(4): 964-970.
doi: 10.11999/JEIT170637
Abstract:
Human Pulse Wave Velocity (PWV) is considered to be one of the important factors that reflects the cardiovascular and cerebrovascular health and the elasticity change of vascular wall. The research of medical pulse wave velocity becomes increasingly hot, such as diabetes, high blood pressure, coronary heart disease, atherosclerosis and other diseases are also closely related. Therefore, the detection of PWV has important and special significance. This paper focuses on signal extraction and signal analysis to study the pulse wave, using the standard clock signal to insert the signal extracted between the brachial artery and radial artery and the dual-mode algorithm of the average among the phase difference of multi-point pulse wave signal, then the PWV is accurately calculated, and its standard deviation is between 0.06 and 0.12. The dual-mode algorithm is superior to the traditional PWV measurement method in real-time, measurement accuracy and stability. It can be used in pulse wave medical research and experiments.
Human Pulse Wave Velocity (PWV) is considered to be one of the important factors that reflects the cardiovascular and cerebrovascular health and the elasticity change of vascular wall. The research of medical pulse wave velocity becomes increasingly hot, such as diabetes, high blood pressure, coronary heart disease, atherosclerosis and other diseases are also closely related. Therefore, the detection of PWV has important and special significance. This paper focuses on signal extraction and signal analysis to study the pulse wave, using the standard clock signal to insert the signal extracted between the brachial artery and radial artery and the dual-mode algorithm of the average among the phase difference of multi-point pulse wave signal, then the PWV is accurately calculated, and its standard deviation is between 0.06 and 0.12. The dual-mode algorithm is superior to the traditional PWV measurement method in real-time, measurement accuracy and stability. It can be used in pulse wave medical research and experiments.
2018, 40(4): 971-978.
doi: 10.11999/JEIT170933
Abstract:
The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation. To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver. One advantage of this approach is that it does not need extensive data collection and complicated prior training. The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
The accurate segmentation of liver in medical Computed Tomography (CT) sequence images is important prerequisite for computer-assisted liver surgery. However, the presence of tissue lesions, the blurred or missing boundary and the adhesion between different organs/tissues poses great challenges to liver segmentation. To address these problems, this paper presents a semi-automatic segmentation method based on the sequential constraints of image sequences, and introduces further a multi-view information fusion method to achieve the accurate segmentation of the liver. One advantage of this approach is that it does not need extensive data collection and complicated prior training. The validation and comparison results on the Sliver07 public data show that the proposed method shows competitive performance, especially when there is liver tumor, blurred or missing liver boundary.
2018, 40(4): 979-874.
doi: 10.11999/JEIT170583
Abstract:
Based on the effectiveness of the fuzzy logic in the field of psychological linguistics research, this paper proposes a new kind of fuzzy logic system without any rules based on fuzzy perception intensity and Webers law, and the method of adaptive control application. Firstly, applying the concept of psychophysics, the knowledge base of fuzzy logic system is constructed by fuzzy perception intensity, which describes experts experience feelings. After fuzzy reasoning, the final output is obtained from defuzzification by generalized Weber's law. Secondly, for a class of nonlinear system, this new fuzzy logic system is adopted to design adaptive controller and parameters adaptive laws. Finally, the feasibility and validity of the method are illustrated through the synchronization simulation about Duffing chaotic systems.
Based on the effectiveness of the fuzzy logic in the field of psychological linguistics research, this paper proposes a new kind of fuzzy logic system without any rules based on fuzzy perception intensity and Webers law, and the method of adaptive control application. Firstly, applying the concept of psychophysics, the knowledge base of fuzzy logic system is constructed by fuzzy perception intensity, which describes experts experience feelings. After fuzzy reasoning, the final output is obtained from defuzzification by generalized Weber's law. Secondly, for a class of nonlinear system, this new fuzzy logic system is adopted to design adaptive controller and parameters adaptive laws. Finally, the feasibility and validity of the method are illustrated through the synchronization simulation about Duffing chaotic systems.
2018, 40(4): 985-991.
doi: 10.11999/JEIT170349
Abstract:
In order to design variable fractional delay filters with low complexity and high accuracy, an efficient design method with controllable cut-off frequency is proposed, which integrates the analytic all-phase filter design, the cubic spline interpolation and Taylor series expansion. In the proposed design, not only the time delay of the filter can be precisely adjusted by setting the delay parameter, but also the tap coefficients of each subfilter in the Farrow structure can be rapidly configured via setting the cut-off frequency parameter, thus the cut-off frequency of the filter can be adjusted flexibly. Numerical simulations show that, the proposed method is especially suitable to design variable fractional delay filters with low or middle cut-off frequencies, and its computation complexity is one order of magnitude lower than that of the existing Weighted Least Squares (WLS) design.
In order to design variable fractional delay filters with low complexity and high accuracy, an efficient design method with controllable cut-off frequency is proposed, which integrates the analytic all-phase filter design, the cubic spline interpolation and Taylor series expansion. In the proposed design, not only the time delay of the filter can be precisely adjusted by setting the delay parameter, but also the tap coefficients of each subfilter in the Farrow structure can be rapidly configured via setting the cut-off frequency parameter, thus the cut-off frequency of the filter can be adjusted flexibly. Numerical simulations show that, the proposed method is especially suitable to design variable fractional delay filters with low or middle cut-off frequencies, and its computation complexity is one order of magnitude lower than that of the existing Weighted Least Squares (WLS) design.
2018, 40(4): 992-1001.
doi: 10.11999/JEIT170701
Abstract:
Ionosphere phase contamination seriously restrains the detection performance of Over-The-Horizon Radar (OTHR) for low detectable targets, such as slow ships, and approaches of time-frequency correction are proposed and classified for effective target detection. A comprehensive performance evaluation of six typical approaches of time-frequency correction based on the key factor analysis of performance comparison is carried out in terms of decontamination effectiveness, robustness analysis and the computational load complexity. This research is of great reference for further development of ionosphere phase decontamination algorithm library and the practical engineering implementation.
Ionosphere phase contamination seriously restrains the detection performance of Over-The-Horizon Radar (OTHR) for low detectable targets, such as slow ships, and approaches of time-frequency correction are proposed and classified for effective target detection. A comprehensive performance evaluation of six typical approaches of time-frequency correction based on the key factor analysis of performance comparison is carried out in terms of decontamination effectiveness, robustness analysis and the computational load complexity. This research is of great reference for further development of ionosphere phase decontamination algorithm library and the practical engineering implementation.
2018, 40(4): 1002-1006.
doi: 10.11999/JEIT170591
Abstract:
Satellite navigation signal waveform is the key factor of signal-in-space quality monitoring. This study mainly focuses on signal waveforms analysis for BDS IGSO-6 frequency B1 by using data of big antenna receiving system. Firstly, code phase average method is proposed to get the high SNR signal waveform, and based on this way the standard chip shape correlator is utilized to extract code element chip. Then, theory between correlation function and code chip is constructed and signal edges characters by using code element chip correlation differences are analyzed. Finally, digital distortions of all BDS satellites are calculated and assessed in detail through signal waveform in time domain.
Satellite navigation signal waveform is the key factor of signal-in-space quality monitoring. This study mainly focuses on signal waveforms analysis for BDS IGSO-6 frequency B1 by using data of big antenna receiving system. Firstly, code phase average method is proposed to get the high SNR signal waveform, and based on this way the standard chip shape correlator is utilized to extract code element chip. Then, theory between correlation function and code chip is constructed and signal edges characters by using code element chip correlation differences are analyzed. Finally, digital distortions of all BDS satellites are calculated and assessed in detail through signal waveform in time domain.
2018, 40(4): 1007-1011.
doi: 10.11999/JEIT170615
Abstract:
This paper presents a novel time domain hybrid method consisting of Finite Difference Time Domain (FDTD) method, Transmission Line (TL) equation, interpolation technique, and Newton iteration method for the coupling simulation of the arbitrary height cable terminated with TVS circuit in shield cavity rapidly. It can achieve a strong synergism computation of electromagnetic field and the responses of the cable and the circuit. The hybrid method uses FDTD method combined with the STL mesh generator technique to model the enclosure of the cavity rapidly and simulate the electromagnetic field distribution in the cavity accurately. The TL equation with interpolation technique is used to set up the coupling model of the cable in the cavity. Then the FDTD method is used to discrete the TL equation to obtain the transient voltage and current responses on the cable. The current-voltage equations are used for the analysis of the TVS circuit. Then the Newton iteration method is utilized to solve the current-voltage equations to obtain the voltage responses on the TVS. The correctness of the hybrid method is confirmed by comparing with the results obtained by electromagnetic simulation software. In testing, the hybrid method can be well applied to the limiting protection design of the loads in the terminal of the cable in shield cavity.
This paper presents a novel time domain hybrid method consisting of Finite Difference Time Domain (FDTD) method, Transmission Line (TL) equation, interpolation technique, and Newton iteration method for the coupling simulation of the arbitrary height cable terminated with TVS circuit in shield cavity rapidly. It can achieve a strong synergism computation of electromagnetic field and the responses of the cable and the circuit. The hybrid method uses FDTD method combined with the STL mesh generator technique to model the enclosure of the cavity rapidly and simulate the electromagnetic field distribution in the cavity accurately. The TL equation with interpolation technique is used to set up the coupling model of the cable in the cavity. Then the FDTD method is used to discrete the TL equation to obtain the transient voltage and current responses on the cable. The current-voltage equations are used for the analysis of the TVS circuit. Then the Newton iteration method is utilized to solve the current-voltage equations to obtain the voltage responses on the TVS. The correctness of the hybrid method is confirmed by comparing with the results obtained by electromagnetic simulation software. In testing, the hybrid method can be well applied to the limiting protection design of the loads in the terminal of the cable in shield cavity.
2018, 40(4): 1012-1016.
doi: 10.11999/JEIT170803
Abstract:
Most triplet Convolutional Neural Network (CNN) based person re-identification algorithms use the Euclidean distance as the similarity measurement between a pair of person images, and utilize the hinge loss function to train CNNs. However, there are two disadvantages in these approaches: the Euclidean distance is not discriminative enough for measuring person similarities; the margin parameter of the hinge loss function must be manually set in advance and it can not be adaptively adjusted. For these, a novel triplet convolutional neural network based person re-identification algorithm is proposed to solve the above two disadvantages for improving the accuracy. First, the normalization hybrid similarity function is proposed to replace Euclidean distance to obtain a more discriminative person similarity measurement. Second, the Log-logistic function is designed to replace the hinge function, which does not need to set the margin parameter so that the joint optimization effect of feature learning and similarity learning is improved. The experimental results on the Auto Detected CUHK03 and VIPeR databases show that the proposed method gains significant improvements in person re-identification accuracy, which verifies the superiority of the proposed method.
Most triplet Convolutional Neural Network (CNN) based person re-identification algorithms use the Euclidean distance as the similarity measurement between a pair of person images, and utilize the hinge loss function to train CNNs. However, there are two disadvantages in these approaches: the Euclidean distance is not discriminative enough for measuring person similarities; the margin parameter of the hinge loss function must be manually set in advance and it can not be adaptively adjusted. For these, a novel triplet convolutional neural network based person re-identification algorithm is proposed to solve the above two disadvantages for improving the accuracy. First, the normalization hybrid similarity function is proposed to replace Euclidean distance to obtain a more discriminative person similarity measurement. Second, the Log-logistic function is designed to replace the hinge function, which does not need to set the margin parameter so that the joint optimization effect of feature learning and similarity learning is improved. The experimental results on the Auto Detected CUHK03 and VIPeR databases show that the proposed method gains significant improvements in person re-identification accuracy, which verifies the superiority of the proposed method.
