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2018 Vol. 40, No. 9
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2018, 40(9)
Abstract:
2018, 40(9): 2033-2041.
doi: 10.11999/JEIT171154
Abstract:
In the process of target tracking, the sensor scheduling algorithm can achieve the tradeoff between the tracking error and the energy consumption so as to extend the service life of the sensor network. The issue can be modeled as a Partially Observable Markov Decision Process (POMDP), which takes both short- and long- term losses of sensor scheduling into account and makes a better decision. A C-QMDP approximation algorithm suitable for continuous state space is proposed. The Markov Chain Monte Carlo (MCMC) method is used to derive the transfer function of belief state and calculate the instantaneous cost. The state discretization method is used to solve the approximation of future cost based on Markov Decision Process (MDP) iteration. Simulation results show that compared to the existing POMDP approximation algorithms, the proposed algorithm can reduce the cumulative losses and computation load in the tracking process by offline computation.
In the process of target tracking, the sensor scheduling algorithm can achieve the tradeoff between the tracking error and the energy consumption so as to extend the service life of the sensor network. The issue can be modeled as a Partially Observable Markov Decision Process (POMDP), which takes both short- and long- term losses of sensor scheduling into account and makes a better decision. A C-QMDP approximation algorithm suitable for continuous state space is proposed. The Markov Chain Monte Carlo (MCMC) method is used to derive the transfer function of belief state and calculate the instantaneous cost. The state discretization method is used to solve the approximation of future cost based on Markov Decision Process (MDP) iteration. Simulation results show that compared to the existing POMDP approximation algorithms, the proposed algorithm can reduce the cumulative losses and computation load in the tracking process by offline computation.
2018, 40(9): 2042-2049.
doi: 10.11999/JEIT171226
Abstract:
To deal with the lack of a secure and efficient data source authentication mechanism in Software-Defined Network (SDN), a packet forwarding authentication mechanism based on cipher identification is proposed. Firstly, a packet forwarding authentication model based on cipher identification is established, where the cipher identification is identified as a passport of IP packets entering and leaving the network. Secondly, the SDN batch anonymous authentication protocol is designed to decentralize the authentication function of the SDN controller to the SDN switch. The SDN switch performs user authentication and cipher identification verification, and quickly filters forgery, falsification, and other illegal packets to improve the unified authentication and management efficiency of the SDN controller, while providing users with the conditions of privacy protection. Thirdly, a scheme for sampling and verifying packets based on cipher identification in any node is proposed, where any attacker can not bypass the packet detection by inferring the sample, to ensure the authenticity of the packet while reducing its processing delay. Finally, safety analysis and performance evaluation are conducted. The results show that this mechanism can quickly detect packet falsification and tampering and resist ID analysis attacks, but at the same time it introduces about 9.6% forwarding delay and less than 10% communication overhead.
To deal with the lack of a secure and efficient data source authentication mechanism in Software-Defined Network (SDN), a packet forwarding authentication mechanism based on cipher identification is proposed. Firstly, a packet forwarding authentication model based on cipher identification is established, where the cipher identification is identified as a passport of IP packets entering and leaving the network. Secondly, the SDN batch anonymous authentication protocol is designed to decentralize the authentication function of the SDN controller to the SDN switch. The SDN switch performs user authentication and cipher identification verification, and quickly filters forgery, falsification, and other illegal packets to improve the unified authentication and management efficiency of the SDN controller, while providing users with the conditions of privacy protection. Thirdly, a scheme for sampling and verifying packets based on cipher identification in any node is proposed, where any attacker can not bypass the packet detection by inferring the sample, to ensure the authenticity of the packet while reducing its processing delay. Finally, safety analysis and performance evaluation are conducted. The results show that this mechanism can quickly detect packet falsification and tampering and resist ID analysis attacks, but at the same time it introduces about 9.6% forwarding delay and less than 10% communication overhead.
2018, 40(9): 2050-2056.
doi: 10.11999/JEIT171149
Abstract:
In Software-Defined Networking (SDN) with distributed control plane, the switches are assigned to controllers using only the quantity distribution of flow requests as the basis of resource allocation. To address this issue, the control resource consumption of flow requests processing with different characteristics is analyzed taking the source and destination of flow as an example, from which a conclusion is drawn that the characteristics distribution of flow should be taken into account when allocating control resource. Then, a flow characteristics aware controller assignment model is designed, and a fast algorithm coping with the fluctuation of flow request is proposed. Simulation results show that when solving with the simulated annealing algorithm, the model can save 10%～20% of control resource compared with the load balancing model; with 10% of resource saving, the proposed algorithm outperforms the simulated annealing algorithm in execution speed and scalability.
In Software-Defined Networking (SDN) with distributed control plane, the switches are assigned to controllers using only the quantity distribution of flow requests as the basis of resource allocation. To address this issue, the control resource consumption of flow requests processing with different characteristics is analyzed taking the source and destination of flow as an example, from which a conclusion is drawn that the characteristics distribution of flow should be taken into account when allocating control resource. Then, a flow characteristics aware controller assignment model is designed, and a fast algorithm coping with the fluctuation of flow request is proposed. Simulation results show that when solving with the simulated annealing algorithm, the model can save 10%～20% of control resource compared with the load balancing model; with 10% of resource saving, the proposed algorithm outperforms the simulated annealing algorithm in execution speed and scalability.
2018, 40(9): 2057-2063.
doi: 10.11999/JEIT170967
Abstract:
In order to utilize storage space and fetch content effectively in Named Data Networking (NDN), this paper constructs a model for caching problem and proposes a greedy algorithm based on topology information. To optimize the algorithm, content popularity is introduced into execution. Furthermore, content hit distance is shortened effectively. This paper simulates a NDN network based on some real topology data with ndnSIM, and compares the proposed algorithm with traditional prob algorithm, default Cache Everything Everywhere (CEE) algorithm and degree based Heterogeneous Storage Size (HSS) algorithm through simulation. The results show that the algorithm proposed in this paper has better performance.
In order to utilize storage space and fetch content effectively in Named Data Networking (NDN), this paper constructs a model for caching problem and proposes a greedy algorithm based on topology information. To optimize the algorithm, content popularity is introduced into execution. Furthermore, content hit distance is shortened effectively. This paper simulates a NDN network based on some real topology data with ndnSIM, and compares the proposed algorithm with traditional prob algorithm, default Cache Everything Everywhere (CEE) algorithm and degree based Heterogeneous Storage Size (HSS) algorithm through simulation. The results show that the algorithm proposed in this paper has better performance.
2018, 40(9): 2064-2071.
doi: 10.11999/JEIT171204
Abstract:
Radio-Frequency (RF) energy harvesting technique is proved a promising way to solve the short network lifetime issue of traditional Wireless Sensor Networks (WSNs) caused by sensors' finite energy. In the existing charging schemes using the mobile RF Energy Transmitter (ET), ET can move to any location along any moving direction in the monitoring area for energy provision. However, in the practical scenario, ET can only move along the existing roads. For the first time, the charging time minimization issue is considered under the moving trajectory constraint. The Mobile Charging (MC) scheme where ET transmits RF energy while moving and the Static Charging (SC) scheme where ET transmits RF energy while unmoving are proposed, whose the moving trajectory and energy provision time are optimized by the proposed efficient algorithms. Simulation results reveal that the total charging delay of proposed schemes is smaller than the baseline scheme of transmitting energy at turning points. The MC scheme has lower computational complexity but has a slightly larger charging delay as compared to the SC scheme.
Radio-Frequency (RF) energy harvesting technique is proved a promising way to solve the short network lifetime issue of traditional Wireless Sensor Networks (WSNs) caused by sensors' finite energy. In the existing charging schemes using the mobile RF Energy Transmitter (ET), ET can move to any location along any moving direction in the monitoring area for energy provision. However, in the practical scenario, ET can only move along the existing roads. For the first time, the charging time minimization issue is considered under the moving trajectory constraint. The Mobile Charging (MC) scheme where ET transmits RF energy while moving and the Static Charging (SC) scheme where ET transmits RF energy while unmoving are proposed, whose the moving trajectory and energy provision time are optimized by the proposed efficient algorithms. Simulation results reveal that the total charging delay of proposed schemes is smaller than the baseline scheme of transmitting energy at turning points. The MC scheme has lower computational complexity but has a slightly larger charging delay as compared to the SC scheme.
2018, 40(9): 2072-2079.
doi: 10.11999/JEIT171135
Abstract:
Some problems of multi-user downlink power allocation and beamforming in a underlay Cognitive Radio Network (CRN) with imperfect Channel State Information (CSI) are addressed. They include ignoring the interferences of the Primary Network (PN) to the Secondary Users (SU), conventional SDR algorithm of convex optimization needing the constraint approximation, the high complexity of the algorithm, and implemented with difficulty, etc. Firstly the term of interference of the PN to the SU is added to the CRN model. The optimization problem is formulated with the worst-case imperfect CSI. Next the constraints of the problem are transformed by means of Lagrange duality. Then, based on the form of the problem, the simple, fast and practical iterative algorithm is obtained by utilizing the duality of uplink-downlink, introducing virtual power, and transforming the optimization problem into the problem of uplink power allocation and beamforming. Numerical simulation results show that it converges faster. It is also found that the errors of the imperfect CSI not only influence the downlink power but also change the feasibility region. The variation of transmitting power of the PN Base Station (PBS) could affect the feasibility region notably.
Some problems of multi-user downlink power allocation and beamforming in a underlay Cognitive Radio Network (CRN) with imperfect Channel State Information (CSI) are addressed. They include ignoring the interferences of the Primary Network (PN) to the Secondary Users (SU), conventional SDR algorithm of convex optimization needing the constraint approximation, the high complexity of the algorithm, and implemented with difficulty, etc. Firstly the term of interference of the PN to the SU is added to the CRN model. The optimization problem is formulated with the worst-case imperfect CSI. Next the constraints of the problem are transformed by means of Lagrange duality. Then, based on the form of the problem, the simple, fast and practical iterative algorithm is obtained by utilizing the duality of uplink-downlink, introducing virtual power, and transforming the optimization problem into the problem of uplink power allocation and beamforming. Numerical simulation results show that it converges faster. It is also found that the errors of the imperfect CSI not only influence the downlink power but also change the feasibility region. The variation of transmitting power of the PN Base Station (PBS) could affect the feasibility region notably.
2018, 40(9): 2080-2087.
doi: 10.11999/JEIT171220
Abstract:
As a key part in Non-Orthogonal Multiple Access (NOMA), user grouping is of particular importance for non-orthogonal multiple access system to improve throughput performance and user fairness. When the number of users and the available resources is increased, the optimal scheduling of user grouping will be infeasible, so a multi-user grouping optimization algorithm for different sub-bands is proposed. According to the user channel gain difference and the restrictions of the multiplexed user number in the same subband, the proposed algorithm firstly performs the process of the initial multi-user grouping to reduce the user’s search space. Then, the optimized combination of the initial grouping users is gradually completed, and the maximum geometric mean user throughput is used as user grouping criterion, which can further enhance the cell-edge user throughput. Simulation results show that the system total throughput and geometric mean user throughput performance of the proposed algorithm can be improved by more than 3% compared with the traditional user grouping algorithms.
As a key part in Non-Orthogonal Multiple Access (NOMA), user grouping is of particular importance for non-orthogonal multiple access system to improve throughput performance and user fairness. When the number of users and the available resources is increased, the optimal scheduling of user grouping will be infeasible, so a multi-user grouping optimization algorithm for different sub-bands is proposed. According to the user channel gain difference and the restrictions of the multiplexed user number in the same subband, the proposed algorithm firstly performs the process of the initial multi-user grouping to reduce the user’s search space. Then, the optimized combination of the initial grouping users is gradually completed, and the maximum geometric mean user throughput is used as user grouping criterion, which can further enhance the cell-edge user throughput. Simulation results show that the system total throughput and geometric mean user throughput performance of the proposed algorithm can be improved by more than 3% compared with the traditional user grouping algorithms.
2018, 40(9): 2088-2095.
doi: 10.11999/JEIT171169
Abstract:
To meet the personal quality requirement of video streaming service under the access and backhaul integrated small base station scene, a user satisfaction maximization algorithm is proposed. The algorithm adjusts dynamically the spectrum resources used for next-cycle queue transmission by analysis the mismatch degree between the actual system reachable rate and user satisfaction demand rate. The corresponding optimization model of the quality satisfaction of all users is established. Then, the Lyapunov stochastic optimization method is used to transform the initial problem into drift plus penalty, the overflow probability constraint is transformed into inequality of variables. Finally, using the proposed user access bandwidth allocation algorithm based on Lagrange dual decomposition and the backhaul and access bandwidth allocation algorithm based on interior point method. The simulation results show that the algorithm can improve the quality satisfaction of all users and ensure the system stability.
To meet the personal quality requirement of video streaming service under the access and backhaul integrated small base station scene, a user satisfaction maximization algorithm is proposed. The algorithm adjusts dynamically the spectrum resources used for next-cycle queue transmission by analysis the mismatch degree between the actual system reachable rate and user satisfaction demand rate. The corresponding optimization model of the quality satisfaction of all users is established. Then, the Lyapunov stochastic optimization method is used to transform the initial problem into drift plus penalty, the overflow probability constraint is transformed into inequality of variables. Finally, using the proposed user access bandwidth allocation algorithm based on Lagrange dual decomposition and the backhaul and access bandwidth allocation algorithm based on interior point method. The simulation results show that the algorithm can improve the quality satisfaction of all users and ensure the system stability.
2018, 40(9): 2096-2104.
doi: 10.11999/JEIT180073
Abstract:
For the issue of joint parameter estimation and symbol detection for multi-antenna signals with channel parameters difference over flat-fading channels, a new joint processing scheme is proposed based on the Variational Bayes (VB) method. The proposed scheme uses directly multiple received signals for the estimation of information symbols, restraining the information loss in conventional decoupled scheme of signals combination and demodulation. The problem is modeled as the joint Maximum A Posteriori (MAP) estimation of information symbols, time-delays, complex channel gains, and noise powers, given multiple observations, and approximately solved by means of VB approach. Based on the criterion of minimum relative entropy, analytical-form of the approximate distributions, i.e., variational distributions, for all unknown parameters are derived. There is no need to determine accurate point estimates of the parameters. Instead, the proposed scheme proceeds iteratively by alternating between the variational distributions of channel parameters and the information symbols. Simulation results show that the proposed joint processing scheme has significant performance improvements in comparison with conventional decoupled or partly joint processing schemes especially with large array sizes and short signal lengths.
For the issue of joint parameter estimation and symbol detection for multi-antenna signals with channel parameters difference over flat-fading channels, a new joint processing scheme is proposed based on the Variational Bayes (VB) method. The proposed scheme uses directly multiple received signals for the estimation of information symbols, restraining the information loss in conventional decoupled scheme of signals combination and demodulation. The problem is modeled as the joint Maximum A Posteriori (MAP) estimation of information symbols, time-delays, complex channel gains, and noise powers, given multiple observations, and approximately solved by means of VB approach. Based on the criterion of minimum relative entropy, analytical-form of the approximate distributions, i.e., variational distributions, for all unknown parameters are derived. There is no need to determine accurate point estimates of the parameters. Instead, the proposed scheme proceeds iteratively by alternating between the variational distributions of channel parameters and the information symbols. Simulation results show that the proposed joint processing scheme has significant performance improvements in comparison with conventional decoupled or partly joint processing schemes especially with large array sizes and short signal lengths.
2018, 40(9): 2105-2111.
doi: 10.11999/JEIT171152
Abstract:
A fast algorithm based on Frequency Locator Polynomial (FLP) for sparse spectrum recovery is proposed. Using the shifted subsampled signals, the FLPs are constructed, thus to locate rapidly the nonzero frequencies. In particular, the nonlinear problem of sparse spectrum recovery is converted into solving a series of linear equations. Experimental results show that the proposed algorithm exhibits higher processing speed and lower error spectrum reconstruction rate than its predecessor BigBand.
A fast algorithm based on Frequency Locator Polynomial (FLP) for sparse spectrum recovery is proposed. Using the shifted subsampled signals, the FLPs are constructed, thus to locate rapidly the nonzero frequencies. In particular, the nonlinear problem of sparse spectrum recovery is converted into solving a series of linear equations. Experimental results show that the proposed algorithm exhibits higher processing speed and lower error spectrum reconstruction rate than its predecessor BigBand.
2018, 40(9): 2112-2118.
doi: 10.11999/JEIT171142
Abstract:
A relay aided secrecy polar coding method is proposed for the communication systems where the relay uses Decode-and-Forward (DF) in probability. It ensures the transmission reliability and improves the secrecy rate. First, the transmitter encodes the secrecy bits in two layers: the first layer is designed over the virtual Binary Erasure Channel (BEC) that generated by the probabilistic DF relay, and the second layer is designed over the real transmission channels. After receiving the codeword, relay decodes and extracts the frozen bits which the legitimate user can not obtain directly in probability, and re-encodes them by classical secrecy polar coding. Finally, the receiver decodes the received codewords from the relay and the transmitter in turn. The theory and simulation results verify that the legitimate user is able to decode reliable, while the eavesdropper can not obtain any information about the secrecy bits. Moreover, the secrecy rate increases as the code length and the relay forwarding probability increase, and it outperforms the classical secrecy polar coding method.
A relay aided secrecy polar coding method is proposed for the communication systems where the relay uses Decode-and-Forward (DF) in probability. It ensures the transmission reliability and improves the secrecy rate. First, the transmitter encodes the secrecy bits in two layers: the first layer is designed over the virtual Binary Erasure Channel (BEC) that generated by the probabilistic DF relay, and the second layer is designed over the real transmission channels. After receiving the codeword, relay decodes and extracts the frozen bits which the legitimate user can not obtain directly in probability, and re-encodes them by classical secrecy polar coding. Finally, the receiver decodes the received codewords from the relay and the transmitter in turn. The theory and simulation results verify that the legitimate user is able to decode reliable, while the eavesdropper can not obtain any information about the secrecy bits. Moreover, the secrecy rate increases as the code length and the relay forwarding probability increase, and it outperforms the classical secrecy polar coding method.
2018, 40(9): 2119-2126.
doi: 10.11999/JEIT171194
Abstract:
Fully homomorphic encryption allows any operation evaluation on encrypted data without decryption. The existing integer-based homomorphic encryption schemes are designed only for two participants namely one party encryption one party decryption (one-to-one), whose computational efficiency is generally low, plaintext space is small, so it can not be applied to big data, cloud computing and other actual scene. Therefore, a full homomorphic encryption scheme with multi-party encryption, one party decryption (multiple to one) is presented. The scheme simplifies the key generation process on the basis of guaranteeing the security, but also gives the range of the number of encrypted parties that can be decrypted accurately in the process of homomorphic operation. Meanwhile, in the random oracle model, the security of the new scheme is proved based on approximate Greatest Common Divisor (GCD) problem. Numerical analysis demonstrates that the presented scheme can not only extend the data traffic, but also improve the efficiency by comparing with the existing schemes. Simulation results show that proposed scheme is more practical in the range of integer, and meets the requirements of the users to the system response. Finally, the plaintext space is expanded to 3 bit, comparing and analysing the experiment with the scheme of 1 bit.
Fully homomorphic encryption allows any operation evaluation on encrypted data without decryption. The existing integer-based homomorphic encryption schemes are designed only for two participants namely one party encryption one party decryption (one-to-one), whose computational efficiency is generally low, plaintext space is small, so it can not be applied to big data, cloud computing and other actual scene. Therefore, a full homomorphic encryption scheme with multi-party encryption, one party decryption (multiple to one) is presented. The scheme simplifies the key generation process on the basis of guaranteeing the security, but also gives the range of the number of encrypted parties that can be decrypted accurately in the process of homomorphic operation. Meanwhile, in the random oracle model, the security of the new scheme is proved based on approximate Greatest Common Divisor (GCD) problem. Numerical analysis demonstrates that the presented scheme can not only extend the data traffic, but also improve the efficiency by comparing with the existing schemes. Simulation results show that proposed scheme is more practical in the range of integer, and meets the requirements of the users to the system response. Finally, the plaintext space is expanded to 3 bit, comparing and analysing the experiment with the scheme of 1 bit.
2018, 40(9): 2127-2134.
doi: 10.11999/JEIT171017
Abstract:
The visual-based scene recognition and localization module is widely used in vehicle safety system. This paper proposes a new method of scene recognition based on local key region and key frame, which is based on the problem of large amount of training data, large matching complexity and low tracking precision. The proposed method meets the real-time requirements with high accuracy. First, the method uses the unsupervised method to extract the significant regions of the single reference sequence captured by the monocular camera as the key regions. The binary features with low correlation in key regions are also extracted to improve the scene matching accuracy and reduce the computational complexity of feature generation and matching. Secondly, key frames in the reference sequence are extracted based on the discrimination score to reduce the retrieval range of the tracking module and improve the efficiency. Practical field tests are done on real data of the light railway system in Hong Kong and the open test data set in Nordland. The experimental results show that the proposed method achieves fast matching and the precision is 9.8% higher than SeqSLAM which is based on global feature.
The visual-based scene recognition and localization module is widely used in vehicle safety system. This paper proposes a new method of scene recognition based on local key region and key frame, which is based on the problem of large amount of training data, large matching complexity and low tracking precision. The proposed method meets the real-time requirements with high accuracy. First, the method uses the unsupervised method to extract the significant regions of the single reference sequence captured by the monocular camera as the key regions. The binary features with low correlation in key regions are also extracted to improve the scene matching accuracy and reduce the computational complexity of feature generation and matching. Secondly, key frames in the reference sequence are extracted based on the discrimination score to reduce the retrieval range of the tracking module and improve the efficiency. Practical field tests are done on real data of the light railway system in Hong Kong and the open test data set in Nordland. The experimental results show that the proposed method achieves fast matching and the precision is 9.8% higher than SeqSLAM which is based on global feature.
2018, 40(9): 2135-2142.
doi: 10.11999/JEIT171143
Abstract:
Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed, constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1% respectively in median distance precision.
Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed, constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1% respectively in median distance precision.
2018, 40(9): 2143-2149.
doi: 10.11999/JEIT171115
Abstract:
A modified Ensemble Kalman Filter (EnKF) theory model based on kinematic equations is proposed to realize the historical fitting analysis and trajectory prediction of the target trajectory in the multi-source observation data scenario. This model is applied to accurately calculate the target motion state parameters (velocity and acceleration), then the target’s follow-up movement is predicted. The multi-source observation data fusion is realized by using the EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF.
A modified Ensemble Kalman Filter (EnKF) theory model based on kinematic equations is proposed to realize the historical fitting analysis and trajectory prediction of the target trajectory in the multi-source observation data scenario. This model is applied to accurately calculate the target motion state parameters (velocity and acceleration), then the target’s follow-up movement is predicted. The multi-source observation data fusion is realized by using the EnKF, which enables the low-precision observation data to be corrected by high-precision observation data, and the accuracy of the corrected data can be calibrated by the statistical information provided by the EnKF.
2018, 40(9): 2150-2157.
doi: 10.11999/JEIT171238
Abstract:
Multiple sources localization is an issue of theoretical importance and practical significance in signal processing. The basis mismatch problem caused by target deviation from the initial grid point is addressed. Based on sparse Bayesian learning framework with Laplace prior, a novel iterative Adaptive Grid Multiple Targets Localization (AGMTL) algorithm is proposed to tackle the practical situation in which the targets deviates from the initial grid point. In essence, AGMTL algorithm implements sparse signal reconstruction and adaptive grid localization dictionary learning jointly. The simulation results show that AGMTL algorithm outperforms the traditional Compressive Sensing (CS) based localization algorithm in the terms of localization error, estimation reliability and noise robustness.
Multiple sources localization is an issue of theoretical importance and practical significance in signal processing. The basis mismatch problem caused by target deviation from the initial grid point is addressed. Based on sparse Bayesian learning framework with Laplace prior, a novel iterative Adaptive Grid Multiple Targets Localization (AGMTL) algorithm is proposed to tackle the practical situation in which the targets deviates from the initial grid point. In essence, AGMTL algorithm implements sparse signal reconstruction and adaptive grid localization dictionary learning jointly. The simulation results show that AGMTL algorithm outperforms the traditional Compressive Sensing (CS) based localization algorithm in the terms of localization error, estimation reliability and noise robustness.
2018, 40(9): 2158-2164.
doi: 10.11999/JEIT171156
Abstract:
Independent Vector Analysis (IVA) is one of the best methods to solve the sort ambiguity of convolutive blind separation in frequency domain. However, it needs more iterations and computing time, and the separation effect is susceptible to the initial value of the separation matrix. This paper proposes an IVA convolutive blind separation algorithm based on step-size adaptive, which uses Joint Approximative Diagonalization of Eigenmatrices (JADE) algorithm to initialize the separation matrix and optimizes adaptively the step step-size parameters. JADE initialization can make the separation matrix have an appropriate initial value, thus avoiding the situation of local convergence; step-size adaptive optimization can significantly improve the convergence speed of the algorithm. Simulation results show that this algorithm improves the separation performance and shortens the operation time significantly.
Independent Vector Analysis (IVA) is one of the best methods to solve the sort ambiguity of convolutive blind separation in frequency domain. However, it needs more iterations and computing time, and the separation effect is susceptible to the initial value of the separation matrix. This paper proposes an IVA convolutive blind separation algorithm based on step-size adaptive, which uses Joint Approximative Diagonalization of Eigenmatrices (JADE) algorithm to initialize the separation matrix and optimizes adaptively the step step-size parameters. JADE initialization can make the separation matrix have an appropriate initial value, thus avoiding the situation of local convergence; step-size adaptive optimization can significantly improve the convergence speed of the algorithm. Simulation results show that this algorithm improves the separation performance and shortens the operation time significantly.
2018, 40(9): 2165-2172.
doi: 10.11999/JEIT171234
Abstract:
When using interactive genetic algorithm to solve big data information retrieval problem, single user needs to complete more human-machine interactive operation to achieve preference information extraction and optimization, thus it is easy to generate the problem of user fatigue and algorithm low efficiency. A multi-user strategy is introduced by making full use of the advantages of group decision to improve the sample utilization efficiency. First of all, multi-user collaborative type is devided into common collaboration or personalized collaboration according to the optimization goal which calculats user similarity and individual similarity based on user’s browsing behaviors. Then, individuals’ interval fitness is forecasted by sharing similar individual of similarity users. Based on phenotype similarity clustering, the large scale population individuals of " interval-interval” fitness assignment strategy is introduced. Finally, the best evaluation individual is recommended according to the similarities between offspring individuals and parent individuals. The proposed method is applied to decorative wallpaper design problem and is compared with existing typical methods. The experimental results confirm that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.
When using interactive genetic algorithm to solve big data information retrieval problem, single user needs to complete more human-machine interactive operation to achieve preference information extraction and optimization, thus it is easy to generate the problem of user fatigue and algorithm low efficiency. A multi-user strategy is introduced by making full use of the advantages of group decision to improve the sample utilization efficiency. First of all, multi-user collaborative type is devided into common collaboration or personalized collaboration according to the optimization goal which calculats user similarity and individual similarity based on user’s browsing behaviors. Then, individuals’ interval fitness is forecasted by sharing similar individual of similarity users. Based on phenotype similarity clustering, the large scale population individuals of " interval-interval” fitness assignment strategy is introduced. Finally, the best evaluation individual is recommended according to the similarities between offspring individuals and parent individuals. The proposed method is applied to decorative wallpaper design problem and is compared with existing typical methods. The experimental results confirm that the proposed algorithm has advantages in improving optimization quality and alleviating user fatigue while improving its efficiency in exploration.
2018, 40(9): 2173-2181.
doi: 10.11999/JEIT170880
Abstract:
Deep learning has a strong ability in the high-dimensional feature vector information extraction and classification. But the training time of deep learning is so long that the optimal hyper-parameters combination can not be found in a short time. To solve these problems, a method of product of experts system based on Restricted Boltzmann Machine (RBM) is proposed. The product of experts theory is combined with the RBM algorithm and the parameter updating way is all adopted the probability value, which leads to the undesirable recognition effect and slightly worse density models, so the parameter updating way is improved. An improved algorithm with momentum terms in different combinations is used not only in the RBM pre-training phase but also in the fine-tuning stage for both classification accuracy enhancement and training time decreasing. Through the recognition experiments on the MNIST database and CMU-PIE face database, the proposed algorithm reduces the training time, and improves the efficiency of hyper-parameters optimization, and then the deep belief network can achieve better classification performance. The result shows that the improved algorithm can improve both accuracy and computation efficiency in dealing with high-dimensional and large amounts of data, the new method is effective.
Deep learning has a strong ability in the high-dimensional feature vector information extraction and classification. But the training time of deep learning is so long that the optimal hyper-parameters combination can not be found in a short time. To solve these problems, a method of product of experts system based on Restricted Boltzmann Machine (RBM) is proposed. The product of experts theory is combined with the RBM algorithm and the parameter updating way is all adopted the probability value, which leads to the undesirable recognition effect and slightly worse density models, so the parameter updating way is improved. An improved algorithm with momentum terms in different combinations is used not only in the RBM pre-training phase but also in the fine-tuning stage for both classification accuracy enhancement and training time decreasing. Through the recognition experiments on the MNIST database and CMU-PIE face database, the proposed algorithm reduces the training time, and improves the efficiency of hyper-parameters optimization, and then the deep belief network can achieve better classification performance. The result shows that the improved algorithm can improve both accuracy and computation efficiency in dealing with high-dimensional and large amounts of data, the new method is effective.
2018, 40(9): 2182-2189.
doi: 10.11999/JEIT170894
Abstract:
Conventionally, the physiological monitoring system obtains singnal by electrode or bandage which is connected with skins and has disadvantages such as: uncomfortable and bad compliance to users. In order to overcome those problems, a new physiological monitoring system, which is based on the principle that micro bend of optical-fiber induced by weak movement of physiology can change the light intensity to get BallistoCardioGram (BCG) signal, is developed. In such system, the respiration rate, heart rate and body movement are obtained by self-adaption detecting the tiny variation of light intensity. In order to protect fiber and enhance the stability and reliability of system, the fiber is embedded into mattress or cushion with a sandwich structure. Simultaneously, it makes the system have high sensitivity that the fiber is uniformly routed with serpentine-curve shape in the middle of mattress or cushion. It is illustrated by the measurement in several hospitals that the mean error of heart rate is –0.26±2.80 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9984 to the standard values. It is exhibited as well that the mean error respiration rate is 0.41±1.49 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9971 to the standard values. It is suggested that the developed system can be senselessly used under zero load and is promised in future.
Conventionally, the physiological monitoring system obtains singnal by electrode or bandage which is connected with skins and has disadvantages such as: uncomfortable and bad compliance to users. In order to overcome those problems, a new physiological monitoring system, which is based on the principle that micro bend of optical-fiber induced by weak movement of physiology can change the light intensity to get BallistoCardioGram (BCG) signal, is developed. In such system, the respiration rate, heart rate and body movement are obtained by self-adaption detecting the tiny variation of light intensity. In order to protect fiber and enhance the stability and reliability of system, the fiber is embedded into mattress or cushion with a sandwich structure. Simultaneously, it makes the system have high sensitivity that the fiber is uniformly routed with serpentine-curve shape in the middle of mattress or cushion. It is illustrated by the measurement in several hospitals that the mean error of heart rate is –0.26±2.80 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9984 to the standard values. It is exhibited as well that the mean error respiration rate is 0.41±1.49 times/min within 95% the confidence interval (±1.96SD) with a correlation 0.9971 to the standard values. It is suggested that the developed system can be senselessly used under zero load and is promised in future.
2018, 40(9): 2190-2197.
doi: 10.11999/JEIT171202
Abstract:
To control the initial azimuth position and the magnitude of the false moving scene, a false moving scene jamming method against Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) is proposed based on double jammers and magnitude modulation. The identical false scenes can be generated in the desired position by the two jammers using delay and shift-frequency modulation. To control the initial azimuth position of the false moving scene, the magnitude ratio of the double scenes are set by controlling the phase of each false target which is generated after two false scenes are superimposed. Theoretical analysis shows that the false moving scene can be generated with the initial azimuth position and magnitude controllable. Next, the jamming effects and influencing factors are analyzed, and the application model of interference algorithm is established. Subsequently, the setting methods of the imaging position, the initial azimuth position and the magnitude compensation coefficient are presented. The jamming effect of the distance between the two jammers in azimuth is also analyzed. Finally, the validity of the proposed method is verified by simulation experiences.
To control the initial azimuth position and the magnitude of the false moving scene, a false moving scene jamming method against Synthetic Aperture Radar-Ground Moving Target Indication (SAR-GMTI) is proposed based on double jammers and magnitude modulation. The identical false scenes can be generated in the desired position by the two jammers using delay and shift-frequency modulation. To control the initial azimuth position of the false moving scene, the magnitude ratio of the double scenes are set by controlling the phase of each false target which is generated after two false scenes are superimposed. Theoretical analysis shows that the false moving scene can be generated with the initial azimuth position and magnitude controllable. Next, the jamming effects and influencing factors are analyzed, and the application model of interference algorithm is established. Subsequently, the setting methods of the imaging position, the initial azimuth position and the magnitude compensation coefficient are presented. The jamming effect of the distance between the two jammers in azimuth is also analyzed. Finally, the validity of the proposed method is verified by simulation experiences.
2018, 40(9): 2198-2205.
doi: 10.11999/JEIT171236
Abstract:
Interrupted Sampling Repeater Jamming (ISRJ) is an advanced intensive false-target jamming with the advantages of fast interference response, anti-agile ability and so on. The radar signal is intermittently sampled with low-rate based on the principle of the under-sampling method, so that the radar can not detect the real targets and the jamming may overload the signal processing system. This article mainly focuses on the Interrupted Sampling Repeater Jamming. A sensitive Doppler sparse waveform is designed based on the ambiguity function theory to suppress the interference, which destroys the continuity of the interference signal output on different Doppler and suppresses the output of the intensive interference. Based on the analysis of the equivalent interval sidelobe, a method of sliding window extraction detection is proposed to achieve effective target detection while anti-jamming. Theoretical analysis and simulation experiments demonstrate the effectiveness of the interference suppression and the target detection performance in the interference.
Interrupted Sampling Repeater Jamming (ISRJ) is an advanced intensive false-target jamming with the advantages of fast interference response, anti-agile ability and so on. The radar signal is intermittently sampled with low-rate based on the principle of the under-sampling method, so that the radar can not detect the real targets and the jamming may overload the signal processing system. This article mainly focuses on the Interrupted Sampling Repeater Jamming. A sensitive Doppler sparse waveform is designed based on the ambiguity function theory to suppress the interference, which destroys the continuity of the interference signal output on different Doppler and suppresses the output of the intensive interference. Based on the analysis of the equivalent interval sidelobe, a method of sliding window extraction detection is proposed to achieve effective target detection while anti-jamming. Theoretical analysis and simulation experiments demonstrate the effectiveness of the interference suppression and the target detection performance in the interference.
2018, 40(9): 2206-2211.
doi: 10.11999/JEIT171136
Abstract:
In order to identify the red signal in the conduction leakage signal of the display power line effectively, a Particle Swarm Optimization-Support Vector Mechine (PSO-SVM) algorithm based on Particle Swarm Optimization (PSO) algorithm for parameter optimization is proposed. Firstly, the conducted leakage signal is filtered, then the PSO-SVM is used to train and classify the conducted leakage signals and compared with the SVM classification. Finally, the display image is reconstructed using PSO-SVM. The result shows that the the red signal can be effectively identified, and the identification rate is significantly higher than the SVM classifier.
In order to identify the red signal in the conduction leakage signal of the display power line effectively, a Particle Swarm Optimization-Support Vector Mechine (PSO-SVM) algorithm based on Particle Swarm Optimization (PSO) algorithm for parameter optimization is proposed. Firstly, the conducted leakage signal is filtered, then the PSO-SVM is used to train and classify the conducted leakage signals and compared with the SVM classification. Finally, the display image is reconstructed using PSO-SVM. The result shows that the the red signal can be effectively identified, and the identification rate is significantly higher than the SVM classifier.
2018, 40(9): 2212-2218.
doi: 10.11999/JEIT171181
Abstract:
The vertical positioning accuracy of BeiDou satellite navigation System (BDS) and the continuity of receiver in the challenge environment can not satisfy the user demand. If atomic clocks are used in the receiver, the high stability of the atomic clock can be used for long time and high precision prediction of receiver clock bias. The positioning accuracy and continuity are improved by using atomic clock and barometric altimeter. This article first analyzes the atomic clocks and barometric altimeter aided BDS positioning algorithm; Then, correction method is proposed for initialization of barometric altimeter, and analysis on the difference of noise type clock is used to determine the clock bias prediction method; Finally, positioning experiment of the atomic clock and barometric altimeter aided BDS in simulation challenge environment is carried out, and the positioning result is analyzed. The results show that BDS can positioning solution to track two visible satellites, and vertical positioning accuracy is significantly improved. The positioning error in the vertical direction is decreased from 8.2 m (RMSE) to 5.2 m, and the fluctuation of the positioning results decreased from 4.6 m to 0.8 m.
The vertical positioning accuracy of BeiDou satellite navigation System (BDS) and the continuity of receiver in the challenge environment can not satisfy the user demand. If atomic clocks are used in the receiver, the high stability of the atomic clock can be used for long time and high precision prediction of receiver clock bias. The positioning accuracy and continuity are improved by using atomic clock and barometric altimeter. This article first analyzes the atomic clocks and barometric altimeter aided BDS positioning algorithm; Then, correction method is proposed for initialization of barometric altimeter, and analysis on the difference of noise type clock is used to determine the clock bias prediction method; Finally, positioning experiment of the atomic clock and barometric altimeter aided BDS in simulation challenge environment is carried out, and the positioning result is analyzed. The results show that BDS can positioning solution to track two visible satellites, and vertical positioning accuracy is significantly improved. The positioning error in the vertical direction is decreased from 8.2 m (RMSE) to 5.2 m, and the fluctuation of the positioning results decreased from 4.6 m to 0.8 m.
2018, 40(9): 2219-2226.
doi: 10.11999/JEIT171231
Abstract:
To solve the problem of radiant target localization using Time Difference Of Arrival (TDOA) measurements from multiple sensors, an algebraic closed-form method based on Weighted Least Squares (WLS) minimizations is proposed, with the priori knowledge of target altitude. In near distance scenario, neglecting the effect of earth curvature, the target altitude can be regarded as one-dimensional coordinate of the target. Based on this condition, the target position is solved by a new two-step WLS algorithm. It does not require initial solution guess, and is computationally attractive due to the non-iterative operation. Simulation results show that the target localization accuracy is greatly improved using target altitude, and the proposed method can reach Cramer-Rao Lower Bound (CRLB) accuracy under small Gaussian measurement noise.
To solve the problem of radiant target localization using Time Difference Of Arrival (TDOA) measurements from multiple sensors, an algebraic closed-form method based on Weighted Least Squares (WLS) minimizations is proposed, with the priori knowledge of target altitude. In near distance scenario, neglecting the effect of earth curvature, the target altitude can be regarded as one-dimensional coordinate of the target. Based on this condition, the target position is solved by a new two-step WLS algorithm. It does not require initial solution guess, and is computationally attractive due to the non-iterative operation. Simulation results show that the target localization accuracy is greatly improved using target altitude, and the proposed method can reach Cramer-Rao Lower Bound (CRLB) accuracy under small Gaussian measurement noise.
2018, 40(9): 2227-2234.
doi: 10.11999/JEIT171233
Abstract:
The traditional method used to extract micro-motion is based on the envelope information of the wideband echo range profile, the estimation accuracy of the traditional method is unsatisfactory. To deal with this problem, a new method for parameter estimation of micro-motion feature is proposed, which is implemented by combining envelope information and phase information of the wideband echo range profile. Firstly, the Keystone transform is performed to each segment obtained by segmenting the envelope of the echo range profile to estimate micro-motion coarsely. Then, the echo phase information is extracted according to the coarse estimation results. The accurate micro-motion curve of each scattering point can be obtained by the principle of phase-derived range. Finally, the estimation of the micro-motion and geometric parameters of the precession target is completed by utilizing the extracted micro-motion curve. Compared with the traditional method, the proposed algorithm can improve the precision of parameter estimation effectively. The effectiveness and stability of the proposed algorithm is verified by simulation experiments.
The traditional method used to extract micro-motion is based on the envelope information of the wideband echo range profile, the estimation accuracy of the traditional method is unsatisfactory. To deal with this problem, a new method for parameter estimation of micro-motion feature is proposed, which is implemented by combining envelope information and phase information of the wideband echo range profile. Firstly, the Keystone transform is performed to each segment obtained by segmenting the envelope of the echo range profile to estimate micro-motion coarsely. Then, the echo phase information is extracted according to the coarse estimation results. The accurate micro-motion curve of each scattering point can be obtained by the principle of phase-derived range. Finally, the estimation of the micro-motion and geometric parameters of the precession target is completed by utilizing the extracted micro-motion curve. Compared with the traditional method, the proposed algorithm can improve the precision of parameter estimation effectively. The effectiveness and stability of the proposed algorithm is verified by simulation experiments.
2018, 40(9): 2235-2241.
doi: 10.11999/JEIT171092
Abstract:
Information geometry based matrix Constant False Alarm Rate (CFAR) detector is an efficient solution to the intractable issue of target detection for K-distributed sea clutter environment. However, most existing matrix CFAR detectors cost heavy computation complexity, which leads to a limitation in practical application. Based on the Neyman-Pearson criterion, the Likelihood Ratio Test (LRT) is analyzed, the relationship between LRT statistic and the Maximum Eigenvalue is derived, and Matrix CFAR Detection method based on the Maximum Eigenvalue (M-MED) is designed. Simulation results verify that the proposed method can achieve better detection performance with relatively lower computational complexity.
Information geometry based matrix Constant False Alarm Rate (CFAR) detector is an efficient solution to the intractable issue of target detection for K-distributed sea clutter environment. However, most existing matrix CFAR detectors cost heavy computation complexity, which leads to a limitation in practical application. Based on the Neyman-Pearson criterion, the Likelihood Ratio Test (LRT) is analyzed, the relationship between LRT statistic and the Maximum Eigenvalue is derived, and Matrix CFAR Detection method based on the Maximum Eigenvalue (M-MED) is designed. Simulation results verify that the proposed method can achieve better detection performance with relatively lower computational complexity.
2018, 40(9): 2242-2249.
doi: 10.11999/JEIT170991
Abstract:
To deal with the problem that most of the existing ship speed estimation algorithms can only estimate the slant range speeds of ships, a ship azimuthal speed estimation method based on local region Doppler centroid for Synthetic Aperture Radar (SAR) images is proposed. Firstly, the variation of Doppler centroid of moving target in local region of SAR image is analyzed and the theoretical formula for estimating the azimuthal speed using the slope of Doppler centroid variation is derived. Then, based on the probability density function of azimuthal power spectrum, an estimation method for the slope of Doppler centroid variation using the maximum likelihood estimation algorithm is presented. Moreover, the estimation accuracy and the applicability of the proposed method are also analyzed. Finally, the proposed method is implemented on simulated and filed data and the estimation results are compared with those obtained by directly calculating the frequency modulation rate. The results show that the proposed method has high estimation accuracy, which verifies the effectiveness of the proposed method.
To deal with the problem that most of the existing ship speed estimation algorithms can only estimate the slant range speeds of ships, a ship azimuthal speed estimation method based on local region Doppler centroid for Synthetic Aperture Radar (SAR) images is proposed. Firstly, the variation of Doppler centroid of moving target in local region of SAR image is analyzed and the theoretical formula for estimating the azimuthal speed using the slope of Doppler centroid variation is derived. Then, based on the probability density function of azimuthal power spectrum, an estimation method for the slope of Doppler centroid variation using the maximum likelihood estimation algorithm is presented. Moreover, the estimation accuracy and the applicability of the proposed method are also analyzed. Finally, the proposed method is implemented on simulated and filed data and the estimation results are compared with those obtained by directly calculating the frequency modulation rate. The results show that the proposed method has high estimation accuracy, which verifies the effectiveness of the proposed method.
2018, 40(9): 2250-2257.
doi: 10.11999/JEIT171209
Abstract:
Traditional Inverse SAR (ISAR) imaging algorithms neglect the impact of high-order rotational phase in the signal, which may make ISAR images of a target defocused. Further, the size of a target can not be obtained from ISAR image directly. In this study, an effective method to achieve the rotation compensation and cross-range scaling for ISAR imaging is proposed. Firstly, all the signals of the target are used to form the Local Average Doppler Trend (LADT) signal. Subsequently, RANdom SAmple Consensus (RANSAC) algorithm is performed to estimate the Doppler rate and effective rotational velocity. Finally, high-precision rotation compensation and cross-range scaling can be accomplished. Simulation and real data experiments validate the effectiveness of the proposed method.
Traditional Inverse SAR (ISAR) imaging algorithms neglect the impact of high-order rotational phase in the signal, which may make ISAR images of a target defocused. Further, the size of a target can not be obtained from ISAR image directly. In this study, an effective method to achieve the rotation compensation and cross-range scaling for ISAR imaging is proposed. Firstly, all the signals of the target are used to form the Local Average Doppler Trend (LADT) signal. Subsequently, RANdom SAmple Consensus (RANSAC) algorithm is performed to estimate the Doppler rate and effective rotational velocity. Finally, high-precision rotation compensation and cross-range scaling can be accomplished. Simulation and real data experiments validate the effectiveness of the proposed method.
2018, 40(9): 2258-2264.
doi: 10.11999/JEIT171002
Abstract:
In order to solve the problem of Direction Of Departure (DOD), Direction Of Arrival (DOA) and Doppler frequency estimation in bistatic MIMO radar, a low complexity method is proposed for joint estimation of the three parameters based on the multi-dimensional Vandermonde structure characteristic of the parameter manifold matrices. First, a third-order tensor is constructed according to the multi- dimensional structure of the echo model. Three equivalent matrices are obtained by cutting the tensor along transmit dimension, receive dimension and pulse dimension respectively. Then, combining the multi-dimensional Vandermonde characteristic with the Khatri-Rao product characteristic of the left-singular matrix of the equivalent matrix, transmit manifold matrix, receive manifold matrix and Doppler manifold matrix are estimated. Finally, the DOD, DOA and Doppler frequency are estimated by Root-MUSIC algorithm. Compared with the existence methods, the proposed algorithm improves obviously the estimation precision, and its computational cost is comparable to that of Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) method in small pulse number. The effectiveness of the proposed method is verified by simulation results.
In order to solve the problem of Direction Of Departure (DOD), Direction Of Arrival (DOA) and Doppler frequency estimation in bistatic MIMO radar, a low complexity method is proposed for joint estimation of the three parameters based on the multi-dimensional Vandermonde structure characteristic of the parameter manifold matrices. First, a third-order tensor is constructed according to the multi- dimensional structure of the echo model. Three equivalent matrices are obtained by cutting the tensor along transmit dimension, receive dimension and pulse dimension respectively. Then, combining the multi-dimensional Vandermonde characteristic with the Khatri-Rao product characteristic of the left-singular matrix of the equivalent matrix, transmit manifold matrix, receive manifold matrix and Doppler manifold matrix are estimated. Finally, the DOD, DOA and Doppler frequency are estimated by Root-MUSIC algorithm. Compared with the existence methods, the proposed algorithm improves obviously the estimation precision, and its computational cost is comparable to that of Estimation of Signal Parameters via Rotation Invariant Techniques (ESPRIT) method in small pulse number. The effectiveness of the proposed method is verified by simulation results.
2018, 40(9): 2265-2272.
doi: 10.11999/JEIT171225
Abstract:
In order to overcome difficulty in extending flank array by traditional Extended Towed Array Measurement (ETAM) technique based on curvilinear maneuverability of Autonomous Underwater Vehicles (AUV), a new ETAM method is proposed by combining estimating phase correction factors in beam domain and linear fitting motion compensation in element domain. The method estimates phase differences in beam domain and implements linear fitting of phase correction factors in element domain to compensate phase error due to curvilinear motion, which can acquire accurate phase correction factors and extend array. Results of simulation and experiment show that no matter straight line navigation or curvilinear maneuverability, the method achieved higher DOA estimation accuracy, angular resolution and signal gain, which is also suitable for multi-target resolving. Especially in low signal-noise-ratio condition, the improvement of this method in performance is more obvious, for which it has strong practicality and environmental tolerance.
In order to overcome difficulty in extending flank array by traditional Extended Towed Array Measurement (ETAM) technique based on curvilinear maneuverability of Autonomous Underwater Vehicles (AUV), a new ETAM method is proposed by combining estimating phase correction factors in beam domain and linear fitting motion compensation in element domain. The method estimates phase differences in beam domain and implements linear fitting of phase correction factors in element domain to compensate phase error due to curvilinear motion, which can acquire accurate phase correction factors and extend array. Results of simulation and experiment show that no matter straight line navigation or curvilinear maneuverability, the method achieved higher DOA estimation accuracy, angular resolution and signal gain, which is also suitable for multi-target resolving. Especially in low signal-noise-ratio condition, the improvement of this method in performance is more obvious, for which it has strong practicality and environmental tolerance.
2018, 40(9): 2273-2280.
doi: 10.11999/JEIT171184
Abstract:
A wideband patch antenna array with low Radar Cross Section (RCS) based on metasurface is proposed. The 2×4 antenna array is composed of two kinds of slotted patch antennas working at different frequency band, realizing the miniaturization and bandwidth broadening of the antenna array. Then, based on the phase cancellation principle, low RCS performance is realized owing to the metasurface consisting of two Artificial Magnetic Conductor (AMC) structures in chessboard configuration. Simulated and measured results show that the working frequency band is expanded from 5.7~6.2 GHz to 5.6~6.6 GHz and radiation performance remains well with metasurface added to. Meanwhile the antenna monostatic RCS is reduced significantly. 3 dB RCS reduction is achieved over the range of 5.3 GHz to 7.0 GHz and the peak reduction is up to 31 dB under X polarization. While the 3 dB RCS reduction range is 5.8~6.9 GHz under Y polarization.
A wideband patch antenna array with low Radar Cross Section (RCS) based on metasurface is proposed. The 2×4 antenna array is composed of two kinds of slotted patch antennas working at different frequency band, realizing the miniaturization and bandwidth broadening of the antenna array. Then, based on the phase cancellation principle, low RCS performance is realized owing to the metasurface consisting of two Artificial Magnetic Conductor (AMC) structures in chessboard configuration. Simulated and measured results show that the working frequency band is expanded from 5.7~6.2 GHz to 5.6~6.6 GHz and radiation performance remains well with metasurface added to. Meanwhile the antenna monostatic RCS is reduced significantly. 3 dB RCS reduction is achieved over the range of 5.3 GHz to 7.0 GHz and the peak reduction is up to 31 dB under X polarization. While the 3 dB RCS reduction range is 5.8~6.9 GHz under Y polarization.
2018, 40(9): 2281-2286.
doi: 10.11999/JEIT171021
Abstract:
For a maximal planar graph G, the operation of extending 3-wheel is a process from G to G∨v, where v is a new vertex embedded in some triangular face xyz of G and G∨v is a graph of order |V(G)|+1 obtained from G by connecting v to each one of x, y, z with one edge. A recursive maximal planar graph is a maximal planar graph obtained from K4 by extending 3-wheel continuously. A (k,l)-recursive maximal planar graph is a recursive maximal planar graph with exactly k vertices of degree 3 so that the distance between arbitrary two vertices of degree k is l. The existence of (k,l)-recursive maximal planar graph is discussed and the structures of (3,2)-as well as (2,3)-recursive maximal planar graphs are described.
For a maximal planar graph G, the operation of extending 3-wheel is a process from G to G∨v, where v is a new vertex embedded in some triangular face xyz of G and G∨v is a graph of order |V(G)|+1 obtained from G by connecting v to each one of x, y, z with one edge. A recursive maximal planar graph is a maximal planar graph obtained from K4 by extending 3-wheel continuously. A (k,l)-recursive maximal planar graph is a recursive maximal planar graph with exactly k vertices of degree 3 so that the distance between arbitrary two vertices of degree k is l. The existence of (k,l)-recursive maximal planar graph is discussed and the structures of (3,2)-as well as (2,3)-recursive maximal planar graphs are described.
