基于稀疏表示分类的雷达欺骗干扰识别方法

doi:10.12305/j.issn.1001-506X.2022.09.12

系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (9): 2791-2799.doi: 10.12305/j.issn.1001-506X.2022.09.12

基于稀疏表示分类的雷达欺骗干扰识别方法

周红平^1,^2,*, 马明辉^1,², 吴若无², 许雄, 郭忠义²

1. 合肥工业大学计算机与信息学院, 安徽合肥 230009
2. 电子信息系统复杂电磁环境效应国家重点实验室, 河南洛阳 471003

收稿日期:2021-12-28 出版日期:2022-09-01 发布日期:2022-09-09
通讯作者: 周红平
作者简介:周红平(1975—), 女, 副研究员, 博士, 主要研究方向为雷达信号处理、雷达干扰识别|马明辉(1996—), 男, 硕士研究生, 主要研究方向为雷达干扰识别|吴若无(1987—), 男, 助理研究员, 硕士, 主要研究方向为复杂电磁环境模拟|许雄(1985—), 男, 助理研究员, 博士, 主要研究方向为电磁态势感知、电磁环境模拟|郭忠义(1981—), 男, 教授, 博士, 主要研究方向为电磁功能应用、电磁态势感知、涡旋雷达系统、先进光通信技术、纳米光子学
基金资助:
电子信息系统复杂电磁环境效应国家重点实验室(CEMEE2020Z0102B)

Deception jamming recognition of radar based on sparse representation classification

Hongping ZHOU^1,^2,*, Minghui MA^1,², Ruowu WU², Xiong XU, Zhongyi GUO²

1. School of Computer and Information, Hefei University of Technology, Hefei 230009, China
2. State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China

Received:2021-12-28 Online:2022-09-01 Published:2022-09-09
Contact: Hongping ZHOU

摘要/Abstract

摘要：

为了应对基于数字射频存储的各种欺骗干扰信号, 提出了一种基于稀疏表示分类的欺骗干扰识别算法。通过小波包分解重构把信号划分为不同频段, 然后对信号提取三阶累积量切片特征构造特征矩阵, 并利用奇异值分解对特征进行降维, 提取主要分量。最后利用稀疏表示分类在不同频段上对信号进行分类识别, 利用决策融合的方法对分类结果进行整合。经验证, 该方法具有很好的抗噪性能, 能够有效识别几种常见的欺骗干扰信号, 在信噪比为0 dB时, 欺骗干扰平均识别率达到90%以上, 并与其他欺骗干扰识别方法进行了对比, 显示了所提方法的优越性。

关键词: 有源干扰识别, 欺骗干扰, 小波包分解, 三阶累积量, 奇异值分解, 稀疏表示

Abstract:

In order to deal with a variety of deception jamming signals based on digital radio frequency memory jammer, this paper presents an algorithm for deception jamming recognition based on sparse representation classification. Firstly, the wavelet packet decomposition and reconstruction are used to divide the signal into different frequency bands, and the feature matrix is constructed by features extracted from the third-order cumulant slice. Then, singular value decomposition is used to reduce the dimension of the feature matrix and extracts the main components. Next, the classification results on each frequency band are obtained by sparse representation classification method, and finally the results are integrated by decision fusion. It has been verified that this method has good anti-noise performance and can effectively identify several common deception jamming signals.When the signal noise ratio is 0 dB, the average recognition rate of deception jamming is more than 90%. Compared with other methods of deception jamming recognition, the superiority of proposed method can be proved.

Key words: active jamming identification, deception jamming, wavelet packet decomposition, third-order cumulant, singular value decomposition, sparse representation

中图分类号:

TN974

周红平, 马明辉, 吴若无, 许雄, 郭忠义. 基于稀疏表示分类的雷达欺骗干扰识别方法[J]. 系统工程与电子技术, 2022, 44(9): 2791-2799.

Hongping ZHOU, Minghui MA, Ruowu WU, Xiong XU, Zhongyi GUO. Deception jamming recognition of radar based on sparse representation classification[J]. Systems Engineering and Electronics, 2022, 44(9): 2791-2799.

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距离拖引速率/(m/s)	信噪比/dB
距离拖引速率/(m/s)	0	1	2	3	4	5	6	7	8
600	0.916	0.935	0.939	0.942	0.960	0.971	0.966	0.978	0.983
800	0.930	0.930	0.942	0.945	0.970	0.967	0.974	0.982	0.991
1 000	0.944	0.960	0.968	0.967	0.982	0.984	0.980	0.988	0.990
1 200	0.934	0.942	0.936	0.965	0.961	0.971	0.972	0.981	0.983

速度拖引速率/ (kHz/s)	信噪比/dB
速度拖引速率/ (kHz/s)	0	1	2	3	4	5	6	7	8
40	0.904	0.915	0.938	0.963	0.957	0.964	0.98	0.981	0.986
50	0.944	0.960	0.968	0.967	0.982	0.984	0.98	0.988	0.990
60	0.917	0.944	0.949	0.963	0.968	0.978	0.97	0.971	0.988

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基于稀疏表示分类的雷达欺骗干扰识别方法

Deception jamming recognition of radar based on sparse representation classification

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