基于一维距离像稀疏表达的无源假目标识别

为了解决敌方释放箔条干扰我方防空反击任务或者敌方舰船设置角反射器阵列干扰我方对海目标打击任务中的雷达抗干扰问题，提出了一种基于雷达一维距离像的稀疏表达的无源假目标识别的方法。首先，分别利用大量关注目标和无源欺骗干扰的雷达一维距离像数据进行稀疏字典学习，分别得到目标和干扰的稀疏字典；然后利用两种稀疏字典分别对未知的雷达一维距离像信号进行稀疏表达；最后分别计算两种稀疏字典对未知信号稀疏表达的重构误差，利用重构误差比值识别目标和干扰类别。仿真结果表明，在目标与无源假目标干扰的回波不混叠、目标与干扰噪声比3 dB条件下，识别无源假目标欺骗干扰的准确率超过90%，证明了该方法抗无源假目标干扰的有效性。

Passive fake target recognition based on one-dimensional distance image sparse representation

A method of passive false target recognition based on sparse representation is proposed to solve two problems of radar anti-jamming that enemy fighters release chaff to interfere our air defense and counterattack task and enemy ships set corner reflector array to interfere our target attack task in the sea. Firstly,sparse dictionary learning is performed using a large amount of radar echo data of target and passive spoofing jamming to obtain a target sparse dictionary and a jamming sparse dictionary respectively. Then two kinds of sparse dictionaries are used to sparsely express the unknown radar signals respectively. Finally,two reconstruction errors of sparse representation by two sparse dictionaries are computed. The target and jamming are identified by the ratio of two reconstruction errors. The simulation results show that the proposed method can recognize passive spoofing jamming with accuracy more than 90% when the echoes of target and passive jamming are not aliased and target-to-jamming ratio of echo is 3 dB,verifying the effectiveness of the method for anti-passive spoofing jamming.