基于多维小波特征的有源雷达欺骗干扰识别
Active Radar Deception Jamming Recognition Based on Multi-dimensional Wavelet Features
刘一兵 1罗强 1胡然 1付琳琳1
作者信息
- 1. 中国人民解放军63892部队,河南洛阳 471003
- 折叠
摘要
针对跟踪工作模式下常见雷达有源欺骗干扰识别问题,通过分析雷达信号和干扰模型,提出一种基于多维小波特征的识别算法.对目标回波多个相干处理间隔中的首个脉冲重复间隔内信号采样累积,构成一维离散序列,利用Mallat算法提取多尺度小波系数,计算不同尺度下小波系数的移位相关系数,并组成特征向量用于分类识别.构建不同参数的信号样本仿真,在信噪比为-5 dB时仍有高于80％的识别正确率.该算法能将距离假目标干扰和距离拖引干扰有效识别,证明了算法的准确性、通用性和严谨性.此外,针对实际情况,可以调整特征向量维度,选择更适合的母小波函数,进一步优化干扰识别效果.
Abstract
According to the identification of radar active deception jamming in tracking mode,by analyzing radar signal and jamming models,we propose a recognition algorithm based on multi-dimensional wavelet features.The signal is sampled and accumulated within the first pulse repetition interval among multiple coherent processing intervals of the target echo to constitute a one-dimensional discrete sequence and the multi-scale wavelet coefficients are extracted using the Mallat algorithm.The shift correlation coefficients of wavelet coefficients at different scales are computed to constitute eigenvector for recognition.Constructing signal samples with different parameters for simulation,the recognition accuracy is still higher than 80％at the signal-to-noise ratio of-5 dB,and the algorithm can effectively distinguish between the range false target and the range gate pull off jamming,which proves the accuracy,versatility and preciseness of the algorithm.Moreover,for the actual situation,we can adjust the dimensions of eigenvector,and select more suitable mother wavelet to further optimize the jamming recognition result.
关键词
雷达有源欺骗干扰/离散小波变换/多尺度分析/特征向量/干扰识别/信噪比Key words
radar active deception jamming/discrete wavelet transform/multi-scale analysis/eigenvector/jamming recognition/signal-to-noise radio(SNR)引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据