In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These j...In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.展开更多
Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a s...Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a series of methods have been proposed to perceive TBM jamming under the condition of soft and fractured surrounding rocks.However,most of these methods cannot predict TBM jamming accurately in advance or perceive the jamming process in real-time.In the present study,a real-time monitoring system(composed of strain gauges,data acquisition,data transmission and data storage)for shield strain was developed and implemented in a TBM at the Lanzhou Water Resource Project in China.The shield strain of the double-shielded TBM was monitored and analysed continuously.The working conditions of the TBM(such as excavation or standstill,jamming or disjamming)were identifiedby analysing the characteristics of shield strain.The perception information was compared to the excavation records.The results indicate that there is good consistency between them.Combined with monitoring information and fieldsurveys,the interaction mechanisms between the shield and surrounding rock were analysed qualitatively.The jamming mechanisms of the two accidents were revealed according to the variation in stable shield strain.A criterion for anticipation TBM jamming was proposed based on the shield strain characteristics(such as trend,amplitude,and magnification).These studies provide references for real-time perception and accurate anticipation of TBM jamming in soft and fractured surrounding rock conditions.展开更多
Layer jamming structures(LJS)are a class of variable stiffness structures that are valuable for adaptive and soft robotic systems.However,existing models for LJS often rely on discrete approximations or are tailored t...Layer jamming structures(LJS)are a class of variable stiffness structures that are valuable for adaptive and soft robotic systems.However,existing models for LJS often rely on discrete approximations or are tailored to specific configurations,limiting their generalizability and computational efficiency.In this study,we propose a contin-uum elastoplastic constitutive model for LJS based on the average-field technique.The model captures both the jamming(no interlayer slipping)and slipping states of LJS,enabling analytical expressions for yield criteria,and dissipated energy density.Finite element simulations in Abaqus incorporating periodic boundary conditions were conducted to validate the theoretical model under various deformation scenarios,including uniaxial shear,multi-directional shear,and coupled shear-normal loading.The results demonstrate strong agreement between numerical and theoretical predictions,effectively capturing the nonlinear transitions in stiffness and energy evo-lution.This continuum framework offers a unified,scalable tool for modeling the mechanical behavior of LJS and supports the design and optimization of stiffness-tunable systems in soft robotics and beyond.展开更多
Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability o...Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.展开更多
Task assignment is critical for multiple unmanned aerial vehicles(UAVs)to perform suppressive jamming against radar network.However,under abrupt state changes of enemy radars,centralized methods often incur high compu...Task assignment is critical for multiple unmanned aerial vehicles(UAVs)to perform suppressive jamming against radar network.However,under abrupt state changes of enemy radars,centralized methods often incur high computational complexity and exhibit limited robustness,rendering them unsuitable for rapid task re-assignment.To address these challenges,an adaptive multi-UAV task assignment and re-assignment scheme for suppressive jamming against intermittent radar network is proposed.Specifically,the system is comprehensively modeled by integrating a motion model describing high-value target trajectory,a reconnaissance model detecting radar state transitions,and a suppressive jamming model characterizing the matching relationships between UAVs and radars.The problem is formulated as a dynamic integer program with time-varying constraints.To solve this,a distributed auction-based task assignment and re-assignment algorithm is proposed,enabling task assignment and re-assignment triggered by sudden radar activations or deactivations.Simulation results demonstrate that the proposed approach achieves jamming performance comparable to centralized methods,outperforms traditional fixed and random strategies,and enables re-assignment in response to abrupt radar state changes.展开更多
This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We b...This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We built a dataset of radar echoes with four comb jamming types and five non-comb interference types.A dual-channel method creates 2D images preserving both magnitude and phase information from the signal spectrum.A CNN classifier with convolutional blocks,batch normalization,and dropout achieves 99.75%accuracy with 1.5%false alarm rate after only 7 training epochs.展开更多
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
To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(...To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.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
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
Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding di...Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.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
In order to solve the problem that the traditional space jamming countermeasure cannot deal with the mainlobe self-protecting jammings,a polarization-space joint mainlobe jamming countermeasure technique based on divi...In order to solve the problem that the traditional space jamming countermeasure cannot deal with the mainlobe self-protecting jammings,a polarization-space joint mainlobe jamming countermeasure technique based on divided dimensions is proposed.Specifically,the digital beam of each row and column is firstly formed by using dual polarization digital receiving in multi-channel.Then,the polarization-space joint cancellation in both azimuth and elevation dimensions is carried out based on the polarization-space joint difference between the target echo and the jamming,as well as the divided dimension feature of the row and column beams.Finally,the sum and difference beams of the full array in the elevation or azimuth dimension are formed by the beams after jamming cancelling,and the monopulse angle measurement is further employed to obtain target angles.The effectiveness of the proposed technique is verified by simulations,indicating that the self-protecting jamming and multiple mainlobe following jammings can be both cancelled simultaneously with the angle measurement unchanged.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
Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber syst...Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.展开更多
Unmanned aerial vehicles(UAVs)are expected to be extensively applied in military and commercial fields,but the issues of information security and energy restrictions hinder such applications.To cope with these issues,...Unmanned aerial vehicles(UAVs)are expected to be extensively applied in military and commercial fields,but the issues of information security and energy restrictions hinder such applications.To cope with these issues,joint beamforming and cooperative jamming are promising physicallayer technologies,which are advantageous in improving data rate,energy efficiency(EE),and information security.In this paper,the information security and EE in multi-antenna UAV communications are jointly considered by formulating an optimization problem of secrecy EE maximization subject to secrecy rate and power constraints.The resulting optimization problem focuses on designing the transmit covariance matrices of confidential messages and artificial interference,and is solved in an iterative manner based on the optimization methods of fractional programming and successive convex approximation(SCA).Numerical simulation results demonstrate that the proposed algorithm achieves a significant gain in secrecy EE while satisfying the requirements of secrecy rate and maximum power.展开更多
Tolerance sensitivity limits the practical application of the cross-eye jammer.Previous literature has demonstrated that retrodirective cross-eye jamming with multiple antenna elements possesses the advantage of loose...Tolerance sensitivity limits the practical application of the cross-eye jammer.Previous literature has demonstrated that retrodirective cross-eye jamming with multiple antenna elements possesses the advantage of loose tolerance requirements compared to traditional cross-eye jamming.However,the previous analysis was limited,because there are still some factors affecting the parameter tolerance of the multiple-element retrodirective cross-eye jamming(MRCJ)system and they have not been investigated completely,such as the loop difference,the baseline ratio and the jammer-to-signal ratio.This paper performs a comprehensive tolerance analysis of the MRCJ system with a nonuniformspacing linear array.Simulation results demonstrate the tolerance effects of the above influence factors and give reasonable advice for easing tolerance sensitivity.展开更多
In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent ja...In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.展开更多
Monopulse radar is widely used in military.Jamming monopulse radar has always been a research hotspot in electronic warfare(EW).Cross-eye jamming has always been considered as the most effective measures to jam with m...Monopulse radar is widely used in military.Jamming monopulse radar has always been a research hotspot in electronic warfare(EW).Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar.In this paper, we propose a multi-group three-tuple crosseye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship.Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically.Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.展开更多
摘要In high-intensity electromagnetic warfare,radar systems are persistently subjected to multi-jammer attacks,including potentially novel unknown jamming types that may emerge exclusively under wartime conditions.These jamming signals severely degrade radar detection performance.Precise recognition of these unknown and compound jamming signals is critical to enhancing the anti-jamming capabilities and overall reliability of radar systems.To address this challenge,this article proposes a novel open-set compound jamming cognition(OSCJC)method.The proposed method employs a detection-classification dual-network architecture,which not only overcomes the false alarm and misdetection issues of traditional closed-set recognition methods when dealing with unknown jamming but also effectively addresses the performance bottleneck of existing open-set recognition techniques focusing on single jamming scenarios in compound jamming environments.To achieve unknown jamming detection,we first employ a consistency labeling strategy to train the detection network using diverse known jamming samples.This strategy enables the network to acquire highly generalizable jamming features,thereby accurately localizing candidate regions for individual jamming components within compound jamming.Subsequently,we introduce contrastive learning to optimize the classification network,significantly enhancing both intra-class clustering and inter-class separability in the jamming feature space.This method not only improves the recognition accuracy of the classification network for known jamming types but also enhances its sensitivity to unknown jamming types.Simulations and experimental data are used to verify the effectiveness of the proposed OSCJC method.Compared with the state-of-the-art open-set recognition methods,the proposed method demonstrates superior recognition accuracy and enhanced environmental adaptability.
基金The support provided by National Natural Science Foundation of China(Grant No.42177140)Natural Science Foundation Innovation and Development Joint Foundation of Hubei Province(Grant No.2024AFD359)Guangxi Science and Technology Program(Grant No.2025JJB160169)is gratefully acknowledged.
摘要Tunnel boring machine(TBM)jamming has become one of the critical factors restricting the tunnelling speed and construction period of squeezing tunnels.To minimize the potential risk and damage of jamming accidents,a series of methods have been proposed to perceive TBM jamming under the condition of soft and fractured surrounding rocks.However,most of these methods cannot predict TBM jamming accurately in advance or perceive the jamming process in real-time.In the present study,a real-time monitoring system(composed of strain gauges,data acquisition,data transmission and data storage)for shield strain was developed and implemented in a TBM at the Lanzhou Water Resource Project in China.The shield strain of the double-shielded TBM was monitored and analysed continuously.The working conditions of the TBM(such as excavation or standstill,jamming or disjamming)were identifiedby analysing the characteristics of shield strain.The perception information was compared to the excavation records.The results indicate that there is good consistency between them.Combined with monitoring information and fieldsurveys,the interaction mechanisms between the shield and surrounding rock were analysed qualitatively.The jamming mechanisms of the two accidents were revealed according to the variation in stable shield strain.A criterion for anticipation TBM jamming was proposed based on the shield strain characteristics(such as trend,amplitude,and magnification).These studies provide references for real-time perception and accurate anticipation of TBM jamming in soft and fractured surrounding rock conditions.
基金supported by the National Natural Science Foundation of China(Grant Nos.52375030 and 52505040)Hebei Provincial Natural Science Foundation(Grant No.E2024203254)S&T Program of Hebei(Grant No.246Z1802G).
摘要Layer jamming structures(LJS)are a class of variable stiffness structures that are valuable for adaptive and soft robotic systems.However,existing models for LJS often rely on discrete approximations or are tailored to specific configurations,limiting their generalizability and computational efficiency.In this study,we propose a contin-uum elastoplastic constitutive model for LJS based on the average-field technique.The model captures both the jamming(no interlayer slipping)and slipping states of LJS,enabling analytical expressions for yield criteria,and dissipated energy density.Finite element simulations in Abaqus incorporating periodic boundary conditions were conducted to validate the theoretical model under various deformation scenarios,including uniaxial shear,multi-directional shear,and coupled shear-normal loading.The results demonstrate strong agreement between numerical and theoretical predictions,effectively capturing the nonlinear transitions in stiffness and energy evo-lution.This continuum framework offers a unified,scalable tool for modeling the mechanical behavior of LJS and supports the design and optimization of stiffness-tunable systems in soft robotics and beyond.
基金Supported by National Key R&D Program of China(Grant Nos.2022YFC3601704,2023YFB4706100)National Natural Science Foundation of China(Grant Nos.U23A20338,62203149).
摘要Existing rehabilitation exoskeleton robots suffer from poor compatibility with the human limb coupling method,large internal power loss,and poor wearable performance,which seriously affect the rehabilitation ability of these robots.Therefore,this study proposes a variable stiffness humancomputer interaction contact unit module(VSHCUM)based on the granular jamming mechanism.It is characterized by a double-layer chamber structure:the inner layer is a granular chamber,and the outer layer is an air chamber.The interaction force is transmitted by embedding a rigid support in the inner layer.Unlike the common flexible-belt interactive contact unit,when the exoskeleton is bound to the patient's limb,vSHCUM can realize adaptive fitting of the patient's limb shape using the pressure change in the double-chamber structure.Simultaneously,by adjusting the vacuum level of the granular chamber,the stiffness of the interactive contact unit can be adjusted by a factor of more than five,and the internal work loss caused by self-pulling deformation during the auxiliary force transfer process can be reduced.
基金supported by Qianyuan Laboratoryby the China Scholarship Council(CSC)(Grant No.202506070040)by the National Key Research and Development Program of China(Grant No.2022YFB3902400)。
摘要Task assignment is critical for multiple unmanned aerial vehicles(UAVs)to perform suppressive jamming against radar network.However,under abrupt state changes of enemy radars,centralized methods often incur high computational complexity and exhibit limited robustness,rendering them unsuitable for rapid task re-assignment.To address these challenges,an adaptive multi-UAV task assignment and re-assignment scheme for suppressive jamming against intermittent radar network is proposed.Specifically,the system is comprehensively modeled by integrating a motion model describing high-value target trajectory,a reconnaissance model detecting radar state transitions,and a suppressive jamming model characterizing the matching relationships between UAVs and radars.The problem is formulated as a dynamic integer program with time-varying constraints.To solve this,a distributed auction-based task assignment and re-assignment algorithm is proposed,enabling task assignment and re-assignment triggered by sudden radar activations or deactivations.Simulation results demonstrate that the proposed approach achieves jamming performance comparable to centralized methods,outperforms traditional fixed and random strategies,and enables re-assignment in response to abrupt radar state changes.
摘要This paper presents a deep learning method to recognize comb spectrum jamming in radar systems.Unlike traditional methods requiring manual feature extraction,our approach learns features directly from signal data.We built a dataset of radar echoes with four comb jamming types and five non-comb interference types.A dual-channel method creates 2D images preserving both magnitude and phase information from the signal spectrum.A CNN classifier with convolutional blocks,batch normalization,and dropout achieves 99.75%accuracy with 1.5%false alarm rate after only 7 training epochs.
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).
摘要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 National Natural Science Foundation of China under Grant U23A20279China Electronics Tian’ao Innovation Theory and Technology Group Fund under Grand 20221193-04-04.
摘要To overcome the challenges of poor real-time performance,limited scalability,and low intelligence in conventional jamming pattern recognition methods,this paper proposes a method based on Wavelet Packet Decomposition(WPD)and enhanced deep learning techniques.In the proposed method,an agent at the receiver processes the received signal using WPD to generate an initial Spectrogram Waterfall(SW),which is subsequently segmented using a sliding window to serve as the input for the jamming recognition network.The network employs a bilateral filter to preprocess the input SW,thereby enhancing the edge features of the jamming signals.To extract abstract features,depthwise separable convolution is utilized instead of traditional convolution,thereby reducing the network’s parameter count and enhancing real-time performance.A pyramid pooling layer is integrated before the fully connected layer to enable the network to process input SW of varying sizes,thus enhancing scalability.During network training,adaptive moment estimation is employed as the optimizer,allowing the network to dynamically adjust the learning rate and accelerate convergence.A comprehensive comparison between the proposed jamming recognition network and six other models is conducted,along with Ablation Experiments(AE)based on numerical simulations.Simulation results demonstrate that the proposed method based on WPD and enhanced deep learning achieves high-precision recognition of various jamming patterns while maintaining a favorable balance among prediction accuracy,network complexity,and prediction time.
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)。
摘要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supported in part by the National Natural Science Foundation of China(No.61906156).
摘要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 by the National Key Research and De-velopment Program of China(2020YFB0505601)。
摘要Complicated electromagnetic environments of the space situational awareness facilities(i.e.,satellite navigation systems,radar)would significantly impact normal operations.Effective monitoring and the corresponding diagnosis of the jamming signals are essential to normal opera-tions and the innovations in anti-jamming equipment.This paper demonstrates a comprehensive survey on jamming monitoring algorithms and applications.The methods in dealing with jamming signals are summarized primarily.Subsequently,the jamming detection,identification,and direc-tion finding techniques are addressed separately.Based on the established studies,we also provide some potential trends of the demonstrated jamming monitoring issues.
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.
摘要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.
摘要In order to solve the problem that the traditional space jamming countermeasure cannot deal with the mainlobe self-protecting jammings,a polarization-space joint mainlobe jamming countermeasure technique based on divided dimensions is proposed.Specifically,the digital beam of each row and column is firstly formed by using dual polarization digital receiving in multi-channel.Then,the polarization-space joint cancellation in both azimuth and elevation dimensions is carried out based on the polarization-space joint difference between the target echo and the jamming,as well as the divided dimension feature of the row and column beams.Finally,the sum and difference beams of the full array in the elevation or azimuth dimension are formed by the beams after jamming cancelling,and the monopulse angle measurement is further employed to obtain target angles.The effectiveness of the proposed technique is verified by simulations,indicating that the self-protecting jamming and multiple mainlobe following jammings can be both cancelled simultaneously with the angle measurement unchanged.
基金supported by the National Natural Science Foundation of China(6237104662201048)the Natural Science Foundation of Chongqing,China(cstc2020jcyj-msxmX0260).
摘要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 by the National Natural Science Foundation of China(62173136)the Natural Science Foundation of Hunan Province(2020JJ2013,2021JJ50047).
摘要Dear Editor,With the advances in computing and communication technologies,the cyber-physical system(CPS),has been used in lots of industrial fields,such as the urban water cycle,internet of things,and human-cyber systems[1],[2],which has to face up to malicious cyber-attacks towards cyber communication of control commands.Specifically,jamming attack is regarded as one of the most common attacks of decreasing network performance.Game theory is widely regarded as a method of accurately describing the interaction between jamming attacker and legitimate user[3].In the cyber layer,the signal game model has been utilized to describe the transmission between the attacker and defender[4].However,most previous game theoretical researches are not feasible to meet the demands of industrial CPSs mainly due to the shared communication network nature.Specifically,it leads to incomplete information for players of game owing to various network-induced phenomena and employed communication protocols.In the physical layer,the secure control[5]and estimation[6]under attack detection have been studied for CPSs.However,these methods not only rely heavily on signals injection detection,but also have no access to smart attackers who launch covert attacks so that data receivers cannot observe the attack behaviour[7].Accordingly,the motivation arising here is to tackle the nested game problem for CPSs subject to jamming attack.
基金Supported by the National Natural Science Foundation of China(No.61871401).
摘要Unmanned aerial vehicles(UAVs)are expected to be extensively applied in military and commercial fields,but the issues of information security and energy restrictions hinder such applications.To cope with these issues,joint beamforming and cooperative jamming are promising physicallayer technologies,which are advantageous in improving data rate,energy efficiency(EE),and information security.In this paper,the information security and EE in multi-antenna UAV communications are jointly considered by formulating an optimization problem of secrecy EE maximization subject to secrecy rate and power constraints.The resulting optimization problem focuses on designing the transmit covariance matrices of confidential messages and artificial interference,and is solved in an iterative manner based on the optimization methods of fractional programming and successive convex approximation(SCA).Numerical simulation results demonstrate that the proposed algorithm achieves a significant gain in secrecy EE while satisfying the requirements of secrecy rate and maximum power.
基金the National Natural Science Foundation of China(61801488,61921001,61601008).
摘要Tolerance sensitivity limits the practical application of the cross-eye jammer.Previous literature has demonstrated that retrodirective cross-eye jamming with multiple antenna elements possesses the advantage of loose tolerance requirements compared to traditional cross-eye jamming.However,the previous analysis was limited,because there are still some factors affecting the parameter tolerance of the multiple-element retrodirective cross-eye jamming(MRCJ)system and they have not been investigated completely,such as the loop difference,the baseline ratio and the jammer-to-signal ratio.This paper performs a comprehensive tolerance analysis of the MRCJ system with a nonuniformspacing linear array.Simulation results demonstrate the tolerance effects of the above influence factors and give reasonable advice for easing tolerance sensitivity.
基金supported by National Natural Science Foundation of China (No. 62071488 and No. 62061013)
摘要In multi-agent confrontation scenarios, a jammer is constrained by the single limited performance and inefficiency of practical application. To cope with these issues, this paper aims to investigate the multi-agent jamming problem in a multi-user scenario, where the coordination between the jammers is considered. Firstly, a multi-agent Markov decision process (MDP) framework is used to model and analyze the multi-agent jamming problem. Secondly, a collaborative multi-agent jamming algorithm (CMJA) based on reinforcement learning is proposed. Finally, an actual intelligent jamming system is designed and built based on software-defined radio (SDR) platform for simulation and platform verification. The simulation and platform verification results show that the proposed CMJA algorithm outperforms the independent Q-learning method and provides a better jamming effect.
摘要Monopulse radar is widely used in military.Jamming monopulse radar has always been a research hotspot in electronic warfare(EW).Cross-eye jamming has always been considered as the most effective measures to jam with monopulse radar.In this paper, we propose a multi-group three-tuple crosseye jamming structure where each group contains three antenna elements with a definite phase and an amplitude relationship.Then, based on the principle of monopulse angle measurement, the error angle is deduced theoretically.Simulations show that such a multi-group three-tuple cross-eye jamming structure performs better than the multi-element cross-eye jamming structure previously proposed, and the analysis of the centroid shows that the centroid of the structure proposed in this paper is more widely distributed in space.