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
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
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
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
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
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
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
提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn...提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn));模拟实验表明,该算法在攻击者攻击范围、网络节点密度以及攻击者位置等度量值变化的情况下,比已有算法具有更好的定位准确度。展开更多
Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel lea...Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel leak was presented. Results It was proved that information channel leak exists in FM fuze because of the nonlinear property of the mixer. The jamming signal was designed based on the channel leak and the jamming mechanism was analyzed in detail. Conclusion This kind of jamming signal can jam the sinusoidal FM fuzes effectively just depending on the jamming signal's feature itself. It's different from the traditional jamming way of simulating echo. Though the sinusoidal FM fuze was just analyzed, the principle is applicable to all FM fuzes. At the same time, it may be used as the reference for FM radar and communication countermeasures.展开更多
Based on the M-ary spread spectrum （M-ary-SS）, direct sequence spread spectrum （DS-SS）, and orthogonal frequency division multiplex （OFDM）, a novel anti-jamming scheme, named orthogonal code time division multi-...Based on the M-ary spread spectrum （M-ary-SS）, direct sequence spread spectrum （DS-SS）, and orthogonal frequency division multiplex （OFDM）, a novel anti-jamming scheme, named orthogonal code time division multi-subchannels spread spectrum modulation （OC-TDMSCSSM）, is proposed to enhance the anti-jamming ability of the unmanned aerial vehicle （UAV） data link. The anti-jamming system with its mathematical model is presented first, and then the signal formats of transmitter and receiver are derived. The receiver＇s bit error rate （BER） is demonstrated and anti-jamming performance analysis is carded out in an additive white Ganssian noise （AWGN） channel. Theoretical research and simulation results show the anti-jamming performance of the proposed scheme better than that of the hybrid direct sequence frequency hopping spread spectrum （DS/FH SS） system. The jamming margin of the OC-TDMSCSSM system is 5 dB higher than that of DS/FH SS system under the condition of Rician channel and full-band jamming, and 6 dB higher under the condition of Rician channel environment and partial-band jamming.展开更多
文摘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 National Natural Science Foundation of China under Grant U23A20279China Electronics Tian’ao Innovation Theory and Technology Group Fund under Grand 20221193-04-04.
文摘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 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.61871401).
文摘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 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supported by the National Natural Science Foundation of China(62173136)the Natural Science Foundation of Hunan Province(2020JJ2013,2021JJ50047).
文摘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.
文摘提出一种基于几何覆盖理论的Jamming攻击定位(GCL,geometry-covering based localization)算法。GCL算法利用计算几何中的凸壳理论,特别是最小包容圆方法,对Jamming攻击者进行定位。理论证明了该算法的正确性和较低的时间复杂度(O(nlogn));模拟实验表明,该算法在攻击者攻击范围、网络节点密度以及攻击者位置等度量值变化的情况下,比已有算法具有更好的定位准确度。
文摘Aim To get the theory base of designing FM fuze's jamming signal, its jamming mechanism was studied. Methods A sinusoidal FM fuze was analyzed in time domain and frequency domain and the concept of channel leak was presented. Results It was proved that information channel leak exists in FM fuze because of the nonlinear property of the mixer. The jamming signal was designed based on the channel leak and the jamming mechanism was analyzed in detail. Conclusion This kind of jamming signal can jam the sinusoidal FM fuzes effectively just depending on the jamming signal's feature itself. It's different from the traditional jamming way of simulating echo. Though the sinusoidal FM fuze was just analyzed, the principle is applicable to all FM fuzes. At the same time, it may be used as the reference for FM radar and communication countermeasures.
基金Aeronautical Science Foundation of China (2007ZC53030)
文摘Based on the M-ary spread spectrum （M-ary-SS）, direct sequence spread spectrum （DS-SS）, and orthogonal frequency division multiplex （OFDM）, a novel anti-jamming scheme, named orthogonal code time division multi-subchannels spread spectrum modulation （OC-TDMSCSSM）, is proposed to enhance the anti-jamming ability of the unmanned aerial vehicle （UAV） data link. The anti-jamming system with its mathematical model is presented first, and then the signal formats of transmitter and receiver are derived. The receiver＇s bit error rate （BER） is demonstrated and anti-jamming performance analysis is carded out in an additive white Ganssian noise （AWGN） channel. Theoretical research and simulation results show the anti-jamming performance of the proposed scheme better than that of the hybrid direct sequence frequency hopping spread spectrum （DS/FH SS） system. The jamming margin of the OC-TDMSCSSM system is 5 dB higher than that of DS/FH SS system under the condition of Rician channel and full-band jamming, and 6 dB higher under the condition of Rician channel environment and partial-band jamming.
