针对目前雷达干扰抑制决策智能化程度低的问题,提出了一种基于双深度优先经验回放和可变贪婪算法改进的双重竞争深度Q网络(double dueling deep Q network,D3QN)决策的雷达干扰抑制方法。首先对雷达目标回波和干扰混合信号进行特征提取...针对目前雷达干扰抑制决策智能化程度低的问题,提出了一种基于双深度优先经验回放和可变贪婪算法改进的双重竞争深度Q网络(double dueling deep Q network,D3QN)决策的雷达干扰抑制方法。首先对雷达目标回波和干扰混合信号进行特征提取;然后根据信号特征通过可变贪婪算法选择动作作用于干扰,并将动作前后的信号特征存储于双深度优先经验回放池后,经过学习决策出最优的干扰抑制策略;最后使用该策略抑制干扰后输出。实验结果表明,该方法有效改善了信号的脉压结果,显著提升了信号的信干噪比,相较于基于D3QN的传统干扰抑制方法,在策略准确率和收敛速度上分别提升了7.3%和8.7%。展开更多
针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM...针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM雷达干扰数学模型。该技术引入阶梯波函数对系统阶数进行调制,在系统延时不变的情况下,可以实现精确位置的欺骗假目标群或固定范围内的随机假目标。通过调整阶梯波函数的各项参数,可以完成对假目标位置、幅度和数量的控制。仿真验证表明,采用均匀阶梯波函数可以实现特定位置的密集假目标,采用非均匀阶梯波函数能够在特定范围内生成随机的假目标。对调频斜率捷变的雷达有着较强的干扰能力,具有较好的工程应用意义。展开更多
随着数字化技术和雷达系统的发展,针对合成孔径雷达(Synthetic Aperture Radar,SAR)的干扰对抗技术不断进步,尤其是基于数字射频存储技术(Digital Radio Frequency Memory,DRFM)产生的有源欺骗干扰为SAR成像系统带来了前所未有的考验。...随着数字化技术和雷达系统的发展,针对合成孔径雷达(Synthetic Aperture Radar,SAR)的干扰对抗技术不断进步,尤其是基于数字射频存储技术(Digital Radio Frequency Memory,DRFM)产生的有源欺骗干扰为SAR成像系统带来了前所未有的考验。针对欺骗干扰开展SAR成像抗干扰方法研究,本文基于相位编码波形与带有循环前缀的正交频分复用(Cyclic Prefix Orthogonal Frequency Division Multiplexing,CP-OFDM)波形进行正交波形设计,提出了相位编码CP-OFDM正交波形。基于CP-OFDM波形的循环前缀(Cyclic Prefix,CP)特征,引入基于线性模型的脉冲压缩方法对相位编码CP-OFDM正交波形的SAR成像回波进行距离向处理,能够实现无旁瓣干扰的自相关脉冲压缩。通过对相位编码CP-OFDM波形的时域相位进行编码优化设计,可以实现不同相位编码CP-OFDM波形之间良好的互相关性能。基于线性模型脉冲压缩方法改善了一种p范数多波形加权循环(p-norm Weighted Cyclic Algorithm,p-WeCAN)波形优化算法,采用该算法对相位编码CP-OFDM波形集的相位编码序列进行优化设计,优化后波形的互相关脉冲压缩结果的峰值水平(Peak Sidelobe Level,PSL)相比于随机相位编码CP-OFDM波形的互相关PSL改善了2 dB左右。CP特性赋予了相位编码CP-OFDM波形良好的自相关脉冲压缩结果,相位编码优化设计提供了良好的互相关脉冲压缩结果,采用该正交波形集进行SAR成像,能够实现对欺骗干扰的抑制。进行了点目标、面目标和基于GF-3回波数据反演的半实测数据的抗欺骗干扰SAR成像仿真,与基于线性调频(Linear Frequency Modulation,LFM)波形的欺骗干扰条件下的成像结果进行对比,验证了相位编码CP-OFDM对欺骗干扰的抑制能力。展开更多
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
接收设备各通道间相位不一致会导致接收及构造信号失真,采用通道均衡技术可解决上述问题,但会产生额外的计算资源开销。以构造信号脉压增益损失模型为基础,研究了通道间相位失配的影响机理,并系统量化分析了采用通道校正的必要性。首先...接收设备各通道间相位不一致会导致接收及构造信号失真,采用通道均衡技术可解决上述问题,但会产生额外的计算资源开销。以构造信号脉压增益损失模型为基础,研究了通道间相位失配的影响机理,并系统量化分析了采用通道校正的必要性。首先,基于线性调频(Linear Frequency Modulated,LFM)信号的时频特性,建立了多通道信号分析模型。然后,分别以双通道和三通道信号为例,系统解析了相位误差分布、信号截断位置与脉压增益损失的定量关系。最后,通过仿真实验验证了理论分析的正确性,为量化评估通道均衡的必要性提供了参考依据。展开更多
文摘针对目前雷达干扰抑制决策智能化程度低的问题,提出了一种基于双深度优先经验回放和可变贪婪算法改进的双重竞争深度Q网络(double dueling deep Q network,D3QN)决策的雷达干扰抑制方法。首先对雷达目标回波和干扰混合信号进行特征提取;然后根据信号特征通过可变贪婪算法选择动作作用于干扰,并将动作前后的信号特征存储于双深度优先经验回放池后,经过学习决策出最优的干扰抑制策略;最后使用该策略抑制干扰后输出。实验结果表明,该方法有效改善了信号的脉压结果,显著提升了信号的信干噪比,相较于基于D3QN的传统干扰抑制方法,在策略准确率和收敛速度上分别提升了7.3%和8.7%。
文摘针对非整数阶盲移频干扰生成的欺骗干扰效果单一,假目标分布规律等问题,提出了一种基于阶梯波盲移频的线性调频(Linear Frequency Modulation,LFM)雷达干扰。设计了均匀阶梯波和非均匀阶梯波两种移频方法,推导了基于阶梯波盲移频的LFM雷达干扰数学模型。该技术引入阶梯波函数对系统阶数进行调制,在系统延时不变的情况下,可以实现精确位置的欺骗假目标群或固定范围内的随机假目标。通过调整阶梯波函数的各项参数,可以完成对假目标位置、幅度和数量的控制。仿真验证表明,采用均匀阶梯波函数可以实现特定位置的密集假目标,采用非均匀阶梯波函数能够在特定范围内生成随机的假目标。对调频斜率捷变的雷达有着较强的干扰能力,具有较好的工程应用意义。
文摘随着数字化技术和雷达系统的发展,针对合成孔径雷达(Synthetic Aperture Radar,SAR)的干扰对抗技术不断进步,尤其是基于数字射频存储技术(Digital Radio Frequency Memory,DRFM)产生的有源欺骗干扰为SAR成像系统带来了前所未有的考验。针对欺骗干扰开展SAR成像抗干扰方法研究,本文基于相位编码波形与带有循环前缀的正交频分复用(Cyclic Prefix Orthogonal Frequency Division Multiplexing,CP-OFDM)波形进行正交波形设计,提出了相位编码CP-OFDM正交波形。基于CP-OFDM波形的循环前缀(Cyclic Prefix,CP)特征,引入基于线性模型的脉冲压缩方法对相位编码CP-OFDM正交波形的SAR成像回波进行距离向处理,能够实现无旁瓣干扰的自相关脉冲压缩。通过对相位编码CP-OFDM波形的时域相位进行编码优化设计,可以实现不同相位编码CP-OFDM波形之间良好的互相关性能。基于线性模型脉冲压缩方法改善了一种p范数多波形加权循环(p-norm Weighted Cyclic Algorithm,p-WeCAN)波形优化算法,采用该算法对相位编码CP-OFDM波形集的相位编码序列进行优化设计,优化后波形的互相关脉冲压缩结果的峰值水平(Peak Sidelobe Level,PSL)相比于随机相位编码CP-OFDM波形的互相关PSL改善了2 dB左右。CP特性赋予了相位编码CP-OFDM波形良好的自相关脉冲压缩结果,相位编码优化设计提供了良好的互相关脉冲压缩结果,采用该正交波形集进行SAR成像,能够实现对欺骗干扰的抑制。进行了点目标、面目标和基于GF-3回波数据反演的半实测数据的抗欺骗干扰SAR成像仿真,与基于线性调频(Linear Frequency Modulation,LFM)波形的欺骗干扰条件下的成像结果进行对比,验证了相位编码CP-OFDM对欺骗干扰的抑制能力。
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.
文摘接收设备各通道间相位不一致会导致接收及构造信号失真,采用通道均衡技术可解决上述问题,但会产生额外的计算资源开销。以构造信号脉压增益损失模型为基础,研究了通道间相位失配的影响机理,并系统量化分析了采用通道校正的必要性。首先,基于线性调频(Linear Frequency Modulated,LFM)信号的时频特性,建立了多通道信号分析模型。然后,分别以双通道和三通道信号为例,系统解析了相位误差分布、信号截断位置与脉压增益损失的定量关系。最后,通过仿真实验验证了理论分析的正确性,为量化评估通道均衡的必要性提供了参考依据。
