Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,whi...Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.展开更多
Jamming suppression is traditionally achieved through the use of spatial filters based on array signal processing theory.In order to achieve better jamming suppression performance,many studies have applied blind sourc...Jamming suppression is traditionally achieved through the use of spatial filters based on array signal processing theory.In order to achieve better jamming suppression performance,many studies have applied blind source separation(BSS)to jamming suppression.BSS can achieve the separation and extraction of the individual source signals from the mixed signal received by the array.This paper proposes a perspective to recognize BSS as spatial band-pass filters(SBPFs)for jamming suppression applications.The theoretical derivation indicates that the processing of mixed signals by BSS can be perceived as the application of a set of SBPFs that gate the source signals at various angles.Simulations are performed using radar jamming suppression as an example.The simulation results suggest that BSS and SBPFs produce approximately the same effects.Simulation results are consistent with theoretical derivation results.展开更多
This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD...This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.展开更多
In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assiste...In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.展开更多
The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input mult...The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input multiple output(MIMO)technology,a specific implementation form of ICAJ system,called communication-aided collaborative jamming system,is designed to transmit C&J signals at the same time and frequency.Different from previous studies which overlook the jamming prior information acquisition process and assume that the prior information is perfect or with bounded error,this paper takes the non-cooperative characteristics of jamming and the consequent difficulty in prior information acquisition into consideration.To analyze the tradeoff between C&J,the integration metric is proposed and then the corresponding system design problem is formulated.However,the non-convexity of problem and the lack of jamming prior information make the optimization tricky.In this case,blind channel estimation(BCE)is introduced to obtain an approximate channel state information(CSI)without interacting with jamming targets and then the neural network embedded with system performance calculation model is developed to establish the correspondence between the estimated CSI and optimal beamforming design.Furthermore,a hybrid data-driven and model-based approach,blind channel estimation-deep learning(BCEDL),is proposed to accomplish the beamforming design based on unsupervised learning for ICAJ system in non-cooperative scenarios.The simulation results show that the BCE-DL algorithm outperforms the conventional algorithms in the presence of CSI estimation errors and is a flexible approach which takes the best of both data-driven and model-based methods to design the ICAJ system.展开更多
Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for t...Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.展开更多
Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures(ECCM)field.Target...Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures(ECCM)field.Target parameters and target direction estimation is difficult in radar MLJ.A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper.The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle.Precisely,the eigen-projection matrix processing(EMP)algorithm is adopted to suppress the MLJ,and the target range is estimated effectively through the beamforming and pulse compression.Then the target angle can be effectively estimated by the atom-reconstruction method.Without any prior knowledge,the MLJ can be canceled,and the angle estimation accuracy is well preserved.Furthermore,the proposed method does not have strict requirement for radar array construction,and it can be applied for linear array and planar array.Moreover,the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth(or elevation)equals to the jamming azimuth(or elevation),because the MLJ is suppressed in spatial plane dimension.展开更多
This paper investigates the jammerassisted multi-channel covert wireless communication(CWC)by exploiting the randomness of sub-channel selection to confuse the warden.In particular,we propose two sub-channel selection...This paper investigates the jammerassisted multi-channel covert wireless communication(CWC)by exploiting the randomness of sub-channel selection to confuse the warden.In particular,we propose two sub-channel selection transmission schemes,named random sub-channel selection(RSS)scheme and maximum sub-channel selection(MSS)scheme,to enhance communication covertness.For each proposed scheme,we first derive closed-form expressions of the transmission outage probability(TOP),the average effective rate,and the minimum average detection error probability(DEP).Then,the average effective covert rate(ECR)is maximized by jointly optimizing the transmit power at the transmitter and the number of sub-channels.Numerical results show that there is an optimal value of the number of sub-channels that maximizes the average ECR.We also find that to achieve the maximum average ECR,a larger number of subchannels are needed facing a stricter covertness constraint.展开更多
Passive jamming is believed to have very good potential in countermeasure community.In this paper,a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed.The amplitud...Passive jamming is believed to have very good potential in countermeasure community.In this paper,a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed.The amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state,and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying effects.Dependency of the decoying effect on corner reflectors’radar cross section and positions are analyzed and simulated.Results show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.展开更多
This paper investigates the problem of Joint Radar Node Selection and Power Allocation(JRNSPA)in the Multiple Radar System(MRS)in the blanket jamming environment.Each radar node independently tracks moving target and ...This paper investigates the problem of Joint Radar Node Selection and Power Allocation(JRNSPA)in the Multiple Radar System(MRS)in the blanket jamming environment.Each radar node independently tracks moving target and subsequently transmits the raw observation data to the fusion center,which formulates a centralized tracking network structure.In order to establish a practical blanket jamming environment,we suppose that each target carries the self-defense jammer which automatically implements blanket jamming to the radar nodes that exceed the preset interception probability.Subsequently,the Predicted Conditional Cramer-Rao Lower Bound(PC-CRLB)is derived and utilized as the tracking accuracy criterion.Aimed at ensuring both the tracking performance and the Low Probability of Intercept(LPI)performance,the resource-saving scheduling model is formulated to minimize the transmit power consumption while meeting the requirements of tracking accuracy.Finally,the Modified Zoutendijk Method Of Feasible Directions(MZMFD)-based two-stage solution technique is adopted to solve the formulated non-convex optimization model.Simulation results show the effectiveness of the proposed JRNSPA scheme.展开更多
In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise p...In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.展开更多
To resist various types of jamming in wireless channels,appropriate constellation modulation is used in wireless communication to ensure a low bit error rate.Due to the complexity and variability of the channel enviro...To resist various types of jamming in wireless channels,appropriate constellation modulation is used in wireless communication to ensure a low bit error rate.Due to the complexity and variability of the channel environment,a simple preset constellation is difficult to adapt to all scenarios,so the online constellation optimization method based on Reinforcement Learning(RL)shows its potential.However,the existing RL technology is difficult to ensure the optimal convergence efficiency.Therefore,in this paper,Dynamic Adversarial Interference(DAJ)waveforms are introduced and the DAJ-RL method is proposed by referring to adversarial training in Deep Learning(DL).The algorithm can converge to the optimal state quickly by self-adaptive power and probability direction of dynamic strong adversary of DAJ.In this paper,a rigorous theoretical proof of the symbol error rate is given and it is shown that the method approaches the mathematical limit.Also,numerical and hardware experiments show that the constellations generated by DAJ-RL have the best error rate at all noise levels.In the end,the proposed DAJ-RL method effectively improves the RL-based anti-jamming modulation for cognitive electronic warfare.展开更多
Soft grippers are favored for handling delicate objects due to their compliance but often have lower load capacities compared to rigid ones.Variable Stiffness Module(VSM)offer a solution,balancing flexibility and load...Soft grippers are favored for handling delicate objects due to their compliance but often have lower load capacities compared to rigid ones.Variable Stiffness Module(VSM)offer a solution,balancing flexibility and load capacity,for which particle jamming is an effective technology for stiffness-tunable robots requiring safe interaction and load capacity.Specific applica-tions,such as rescue scenarios,require quantitative analysis to optimize VSM design parameters,which previous analytical models cannot effectively handle.To address this,a Grey-box model is proposed to analyze the mechanical response of the particle-jamming-based VSM by combining a White-box approach based on the virtual work principle with a Black-box approach that uses a shallow neural network method.The Grey-box model demonstrates a high level of accuracy in predict-ing the VSM force-height mechanical response curves,with errors below 15%in almost 90%of the cases and a maximum error of less than 25%.The model is used to optimize VSM design parameters,particularly those unexplored combinations.Our results from the load capacity and force distribution comparison tests indicate that the VSM,optimized through our methods,quantitatively meets the practical engineering requirements.展开更多
There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve th...There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve the above problem,this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes.Firstly,in the range-doppler domain,the real and false targets are truncated along the range dimension,and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response.Then,a two-channel feature fusion network is designed for real and false target recognition.Finally,a Multi-Coherent Processing Interval Joint Decision Method(M-CPIJDM)based on temporal information is proposed to improve the recognition performance.Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds:distance false target,velocity false target,distance-velocity composite false target,and forwarding dense false target.展开更多
针对复杂电磁环境下雷达复合干扰识别困难和网络模型复杂度高的问题,将多标签分类与改进的ShuffleNet V2相结合,提出一种轻量化的多标签ShuffleNet(multi-labeling ShuffleNet, ML-SNet)雷达复合干扰识别算法。首先,使用轻量化的Shuffle...针对复杂电磁环境下雷达复合干扰识别困难和网络模型复杂度高的问题,将多标签分类与改进的ShuffleNet V2相结合,提出一种轻量化的多标签ShuffleNet(multi-labeling ShuffleNet, ML-SNet)雷达复合干扰识别算法。首先,使用轻量化的ShuffleNet V2作为主干网络,引入SimAM(similarity-based attention module)注意力机制,提高网络特征提取能力。其次,使用漏斗激活线性整流函数(funnel activation rectified linear unit, FReLU)代替线性整流单元(rectified linear unit, ReLU)激活函数,减少特征图的信息损失。最后,使用多标签分类算法对网络输出进行分类,得到识别结果。实验结果表明,在干噪比范围为-10~10 dB的情况下,所提算法对15类雷达复合干扰的平均识别率为97.9%。与其他网络相比,所提算法具有较低的计算复杂度,而且识别性能表现最佳。展开更多
基金supported by the National Natural Science Foundation of China(62001481,61890542,62071475)the Natural Science Foundation of Hunan Province(2022JJ40561)the Research Program of National University of Defense Technology(ZK22-46).
文摘Nonperiodic interrupted sampling repeater jamming(ISRJ)against inverse synthetic aperture radar(ISAR)can obtain two-dimensional blanket jamming performance by joint fast and slow time domain interrupted modulation,which is obviously dif-ferent from the conventional multi-false-target deception jam-ming.In this paper,a suppression method against this kind of novel jamming is proposed based on inter-pulse energy function and compressed sensing theory.By utilizing the discontinuous property of the jamming in slow time domain,the unjammed pulse is separated using the intra-pulse energy function diffe-rence.Based on this,the two-dimensional orthogonal matching pursuit(2D-OMP)algorithm is proposed.Further,it is proposed to reconstruct the ISAR image with the obtained unjammed pulse sequence.The validity of the proposed method is demon-strated via the Yake-42 plane data simulations.
基金supported by the National Natural Science Foundation of China(6237104662201048)the Natural Science Foundation of Chongqing,China(cstc2020jcyj-msxmX0260).
文摘Jamming suppression is traditionally achieved through the use of spatial filters based on array signal processing theory.In order to achieve better jamming suppression performance,many studies have applied blind source separation(BSS)to jamming suppression.BSS can achieve the separation and extraction of the individual source signals from the mixed signal received by the array.This paper proposes a perspective to recognize BSS as spatial band-pass filters(SBPFs)for jamming suppression applications.The theoretical derivation indicates that the processing of mixed signals by BSS can be perceived as the application of a set of SBPFs that gate the source signals at various angles.Simulations are performed using radar jamming suppression as an example.The simulation results suggest that BSS and SBPFs produce approximately the same effects.Simulation results are consistent with theoretical derivation results.
基金supported in part by the National Natural Science Foundation of China(No.61906156).
文摘This paper studies the problem of jamming decision-making for dynamic multiple communication links in wireless communication networks(WCNs).We propose a novel jamming channel allocation and power decision-making(JCAPD)approach based on multi-agent deep reinforcement learning(MADRL).In high-dynamic and multi-target aviation communication environments,the rapid changes in channels make it difficult for sensors to accurately capture instantaneous channel state information.This poses a challenge to make centralized jamming decisions with single-agent deep reinforcement learning(DRL)approaches.In response,we design a distributed multi-agent decision architecture(DMADA).We formulate multi-jammer resource allocation as a multiagent Markov decision process(MDP)and propose a fingerprint-based double deep Q-Network(FBDDQN)algorithm for solving it.Each jammer functions as an agent that interacts with the environment in this framework.Through the design of a reasonable reward and training mechanism,our approach enables jammers to achieve distributed cooperation,significantly improving the jamming success rate while considering jamming power cost,and reducing the transmission rate of links.Our experimental results show the FBDDQN algorithm is superior to the baseline methods.
基金supported in part by the National Natural Science Foundation of China under Grant 62071253,Grant 62371252 and Grant 62271268in part by the Jiangsu Provincial Key Research and Development Program under Grant BE2022800in part by the Jiangsu Provincial 333 Talent Project.
文摘In this paper,we examine an illegal wireless communication network consisting of an illegal user receiving illegal signals from an illegal station and propose an active reconfigurable intelligent surface(ARIS)-assisted multi-antenna jamming(MAJ)scheme denoted by ARIS-MAJ to interfere with the illegal signal transmission.In order to strike a balance between the jamming performance and the energy consumption,we consider a so-called jamming energy efficiency(JEE)which is defined as the ratio of achievable rate reduced by the jamming system to the corresponding power consumption.We formulate an optimization problem to maximize the JEE for the proposed ARIS-MAJ scheme by jointly optimizing the jammer’s beamforming vector and ARIS’s reflecting coefficients under the constraint that the jamming power received at the illegal user is lower than the illegal user’s detection threshold.To address the non-convex optimization problem,we propose the Dinkelbach-based alternating optimization(AO)algorithm by applying the semidefinite relaxation(SDR)algorithm with Gaussian randomization method.Numerical results validate that the proposed ARIS-MAJ scheme outperforms the passive reconfigurable intelligent surface(PRIS)-assisted multi-antenna jamming(PRIS-MAJ)scheme and the conventional multiantenna jamming scheme without RIS(NRIS-MAJ)in terms of the JEE.
基金supported by the National Natural Science Foundation of China(No.62171462,No.62401626,No.62271501)the Key Technologies R&D Program of Jiangsu(Prospective and Key Technologies for Industry)under Grants BE2023022 and BE2023022-4the Natural Science Foundation of Jiangsu Province(No.BK20240200)。
文摘The integrated communication and jamming(ICAJ)system recently has been proposed to enable communication and jamming(C&J)to reinforce each other in one system.By exploiting the diversity gain of multiple input multiple output(MIMO)technology,a specific implementation form of ICAJ system,called communication-aided collaborative jamming system,is designed to transmit C&J signals at the same time and frequency.Different from previous studies which overlook the jamming prior information acquisition process and assume that the prior information is perfect or with bounded error,this paper takes the non-cooperative characteristics of jamming and the consequent difficulty in prior information acquisition into consideration.To analyze the tradeoff between C&J,the integration metric is proposed and then the corresponding system design problem is formulated.However,the non-convexity of problem and the lack of jamming prior information make the optimization tricky.In this case,blind channel estimation(BCE)is introduced to obtain an approximate channel state information(CSI)without interacting with jamming targets and then the neural network embedded with system performance calculation model is developed to establish the correspondence between the estimated CSI and optimal beamforming design.Furthermore,a hybrid data-driven and model-based approach,blind channel estimation-deep learning(BCEDL),is proposed to accomplish the beamforming design based on unsupervised learning for ICAJ system in non-cooperative scenarios.The simulation results show that the BCE-DL algorithm outperforms the conventional algorithms in the presence of CSI estimation errors and is a flexible approach which takes the best of both data-driven and model-based methods to design the ICAJ system.
基金the National Natural Science Foundation of China(Grant No.62101579).
文摘Offboard active decoys(OADs)can effectively jam monopulse radars.However,for missiles approaching from a particular direction and distance,the OAD should be placed at a specific location,posing high requirements for timing and deployment.To improve the response speed and jamming effect,a cluster of OADs based on an unmanned surface vehicle(USV)is proposed.The formation of the cluster determines the effectiveness of jamming.First,based on the mechanism of OAD jamming,critical conditions are identified,and a method for assessing the jamming effect is proposed.Then,for the optimization of the cluster formation,a mathematical model is built,and a multi-tribe adaptive particle swarm optimization algorithm based on mutation strategy and Metropolis criterion(3M-APSO)is designed.Finally,the formation optimization problem is solved and analyzed using the 3M-APSO algorithm under specific scenarios.The results show that the improved algorithm has a faster convergence rate and superior performance as compared to the standard Adaptive-PSO algorithm.Compared with a single OAD,the optimal formation of USV-OAD cluster effectively fills the blind area and maximizes the use of jamming resources.
基金supported by the National Natural Science Foundation of China(6207148262001510)the Civil Aviation Administration o f China(U1733116)。
文摘Mainlobe jamming(MLJ)brings a big challenge for radar target detection,tracking,and identification.The suppression of MLJ is a hard task and an open problem in the electronic counter-counter measures(ECCM)field.Target parameters and target direction estimation is difficult in radar MLJ.A target parameter estimation method via atom-reconstruction in radar MLJ is proposed in this paper.The proposed method can suppress the MLJ and simultaneously provide high estimation accuracy of target range and angle.Precisely,the eigen-projection matrix processing(EMP)algorithm is adopted to suppress the MLJ,and the target range is estimated effectively through the beamforming and pulse compression.Then the target angle can be effectively estimated by the atom-reconstruction method.Without any prior knowledge,the MLJ can be canceled,and the angle estimation accuracy is well preserved.Furthermore,the proposed method does not have strict requirement for radar array construction,and it can be applied for linear array and planar array.Moreover,the proposed method can effectively estimate the target azimuth and elevation simultaneously when the target azimuth(or elevation)equals to the jamming azimuth(or elevation),because the MLJ is suppressed in spatial plane dimension.
文摘This paper investigates the jammerassisted multi-channel covert wireless communication(CWC)by exploiting the randomness of sub-channel selection to confuse the warden.In particular,we propose two sub-channel selection transmission schemes,named random sub-channel selection(RSS)scheme and maximum sub-channel selection(MSS)scheme,to enhance communication covertness.For each proposed scheme,we first derive closed-form expressions of the transmission outage probability(TOP),the average effective rate,and the minimum average detection error probability(DEP).Then,the average effective covert rate(ECR)is maximized by jointly optimizing the transmit power at the transmitter and the number of sub-channels.Numerical results show that there is an optimal value of the number of sub-channels that maximizes the average ECR.We also find that to achieve the maximum average ECR,a larger number of subchannels are needed facing a stricter covertness constraint.
基金supported by the Equipment Pre-research Project(GK202002A020068)。
文摘Passive jamming is believed to have very good potential in countermeasure community.In this paper,a passive angular blinking jamming method based on electronically controlled corner reflectors is proposed.The amplitude of the incident wave can be modulated by switching the corner reflector between the penetration state and the reflection state,and the ensemble of multiple corner reflectors with towing rope can result in complex angle decoying effects.Dependency of the decoying effect on corner reflectors’radar cross section and positions are analyzed and simulated.Results show that the angle measured by a monopulse radar can be significantly interfered by this method while the automatic tracking is employed.
基金This study was supported by the National Natural Science Foundation of China(No.62001506).
文摘This paper investigates the problem of Joint Radar Node Selection and Power Allocation(JRNSPA)in the Multiple Radar System(MRS)in the blanket jamming environment.Each radar node independently tracks moving target and subsequently transmits the raw observation data to the fusion center,which formulates a centralized tracking network structure.In order to establish a practical blanket jamming environment,we suppose that each target carries the self-defense jammer which automatically implements blanket jamming to the radar nodes that exceed the preset interception probability.Subsequently,the Predicted Conditional Cramer-Rao Lower Bound(PC-CRLB)is derived and utilized as the tracking accuracy criterion.Aimed at ensuring both the tracking performance and the Low Probability of Intercept(LPI)performance,the resource-saving scheduling model is formulated to minimize the transmit power consumption while meeting the requirements of tracking accuracy.Finally,the Modified Zoutendijk Method Of Feasible Directions(MZMFD)-based two-stage solution technique is adopted to solve the formulated non-convex optimization model.Simulation results show the effectiveness of the proposed JRNSPA scheme.
基金supported by Shandong Provincial Natural Science Foundation(ZR2020MF015)Aerospace Technology Group Stability Support Project(ZY0110020009).
文摘In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.
基金supported by the National Natural Science Foundation of China(Grant No.12004422)by the Beijing Nova Program of Science and Technology(Grant No.Z191100001119129)。
文摘To resist various types of jamming in wireless channels,appropriate constellation modulation is used in wireless communication to ensure a low bit error rate.Due to the complexity and variability of the channel environment,a simple preset constellation is difficult to adapt to all scenarios,so the online constellation optimization method based on Reinforcement Learning(RL)shows its potential.However,the existing RL technology is difficult to ensure the optimal convergence efficiency.Therefore,in this paper,Dynamic Adversarial Interference(DAJ)waveforms are introduced and the DAJ-RL method is proposed by referring to adversarial training in Deep Learning(DL).The algorithm can converge to the optimal state quickly by self-adaptive power and probability direction of dynamic strong adversary of DAJ.In this paper,a rigorous theoretical proof of the symbol error rate is given and it is shown that the method approaches the mathematical limit.Also,numerical and hardware experiments show that the constellations generated by DAJ-RL have the best error rate at all noise levels.In the end,the proposed DAJ-RL method effectively improves the RL-based anti-jamming modulation for cognitive electronic warfare.
基金supported by the National Key R&D Program of China(Grant No.2019YFB1311200).
文摘Soft grippers are favored for handling delicate objects due to their compliance but often have lower load capacities compared to rigid ones.Variable Stiffness Module(VSM)offer a solution,balancing flexibility and load capacity,for which particle jamming is an effective technology for stiffness-tunable robots requiring safe interaction and load capacity.Specific applica-tions,such as rescue scenarios,require quantitative analysis to optimize VSM design parameters,which previous analytical models cannot effectively handle.To address this,a Grey-box model is proposed to analyze the mechanical response of the particle-jamming-based VSM by combining a White-box approach based on the virtual work principle with a Black-box approach that uses a shallow neural network method.The Grey-box model demonstrates a high level of accuracy in predict-ing the VSM force-height mechanical response curves,with errors below 15%in almost 90%of the cases and a maximum error of less than 25%.The model is used to optimize VSM design parameters,particularly those unexplored combinations.Our results from the load capacity and force distribution comparison tests indicate that the VSM,optimized through our methods,quantitatively meets the practical engineering requirements.
基金supported by the Basic Research Program,China(No.514010503-208)the China Aerospace Science and Technology Corporation Stabilization Support Project(No.ZY0110020009)the Equipment Pre-research Project,China(No.304060201).
文摘There are many types of radar active deception false target jamming that are highly correlated with the real target.Recognizing the real and false targets under a low Signal-to-Noise Ratio(SNR)is difficult.To solve the above problem,this article proposes a real/false target recognition method based on the features of multi-pulse joint frequency response by analyzing the differences in the scattering characteristics and modeling real target echoes as a synthesis of multi-scattering center echoes.Firstly,in the range-doppler domain,the real and false targets are truncated along the range dimension,and a fast Fourier transform is performed to extract the features of multi-pulse joint frequency response.Then,a two-channel feature fusion network is designed for real and false target recognition.Finally,a Multi-Coherent Processing Interval Joint Decision Method(M-CPIJDM)based on temporal information is proposed to improve the recognition performance.Experiments using the measured data show that the proposed method can well recognize real and false target signals under four jamming backgrounds:distance false target,velocity false target,distance-velocity composite false target,and forwarding dense false target.
文摘针对复杂电磁环境下雷达复合干扰识别困难和网络模型复杂度高的问题,将多标签分类与改进的ShuffleNet V2相结合,提出一种轻量化的多标签ShuffleNet(multi-labeling ShuffleNet, ML-SNet)雷达复合干扰识别算法。首先,使用轻量化的ShuffleNet V2作为主干网络,引入SimAM(similarity-based attention module)注意力机制,提高网络特征提取能力。其次,使用漏斗激活线性整流函数(funnel activation rectified linear unit, FReLU)代替线性整流单元(rectified linear unit, ReLU)激活函数,减少特征图的信息损失。最后,使用多标签分类算法对网络输出进行分类,得到识别结果。实验结果表明,在干噪比范围为-10~10 dB的情况下,所提算法对15类雷达复合干扰的平均识别率为97.9%。与其他网络相比,所提算法具有较低的计算复杂度,而且识别性能表现最佳。
