基于盲源分离结合奇异谱分析的雷达多分量信号识别方法

doi:10.12305/j.issn.1001-506X.2025.04.13

摘要/Abstract

摘要：

针对阵列在同波束内同时接收到多个雷达信号、造成时域混叠, 难以进行信号检测与参数测量, 进而导致信号调制类型识别困难的问题, 提出一种基于盲源分离结合奇异谱分析的雷达多分量信号识别方法。首先, 利用奇异谱分析对接收到的阵列信号进行降噪处理, 再使用盲源分离方法对混叠的多分量信号进行分离；然后，对分离信号进行时频变换，得到信号时频图；最后，将时频图作为深度学习网络的输入, 对信号进行识别。仿真结果表明, 在5 dB下, 所提方法对同波束内接收的多分量信号的平均识别率达到92.67%, 有较好的识别效果。

关键词: 盲源分离, 信号识别, 时频分析, 深度学习

Abstract:

The array receives multiple radar signals in the same beam at the same time, which results in time domain aliasing. It is difficult to perform signal detection and parameter measurement, resulting in difficult in recognizing signal modulation types. Aiming at the above problem, a radar multi-component signal recogni-tion method based on blind source separation (BSS) combined with singular spectrum analysis is proposed. Firstly, the singular spectrum analysis is used to denoise the received array signal, and then the BSS method is used to separate the aliasing multi-component signal. Secondly, the time-frequency transform of the separated signal is carried out to obtain the time-frequency diagram of the signal. Finally, the time-frequency diagram is used as the input of the deep learning network to recognize the signal. Simulation results show that the average recognition rate of multi-component signal received in the same beam reaches 92.67% at 5 dB and the proposed method has a good recognition effect.

Key words: blind source separation (BSS), signal recognition, time-frequency analysis, deep learning

中图分类号:

TN971

聂千祁, 沙明辉, 朱应申, 王崇宇, 崔念强. 基于盲源分离结合奇异谱分析的雷达多分量信号识别方法[J]. 系统工程与电子技术, 2025, 47(4): 1168-1175.

Qianqi NIE, Minghui SHA, Yingshen ZHU, Chongyu WANG, Nianqiang CUI. Radar multi-component signal recognition method based on blind source separation combined with singular spectrum analysis[J]. Systems Engineering and Electronics, 2025, 47(4): 1168-1175.

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信号类型	参数	取值范围
LFM	起始频率/MHz	200
NLFM	带宽/MHz	[30, 100]
BPSK	载频/MHz	200
BPSK	巴克码长度	{7, 11, 13}

参数类型	LFM	NLFM	BPSK
幅度/dB	1.422 0	1.307 7	1.295 5
脉宽/μs	2.488 0	2.988 0	2.984 0
频率/MHz	277.04	198.16	199.97

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