基于LSTM和残差网络的雷达有源干扰识别

doi:10.12305/j.issn.1001-506X.2023.02.12

摘要/Abstract

摘要：

针对目前雷达干扰识别方法存在人工特征提取难、强噪声环境下识别率不高的问题, 提出了一种基于长短时记忆(long short-term memory, LSTM)网络和残差网络相结合的雷达有源干扰识别方法。输入有源压制干扰原始时域序列数据, 搭建深度学习网络模型对不同干噪比下的干扰信号进行分类识别。仿真结果表明: 在干噪比为0 dB的情况下, 该方法对4类雷达有源干扰信号的识别准确率均高于98.3%, 与单纯的残差网络和卷积神经网络(convolutional neural networks, CNN)等其他深度学习算法相比，具有更佳的网络性能, 验证了该算法的有效性。

关键词: 干扰识别, 深度学习, 长短时记忆, 残差网络

Abstract:

Aiming at the problem that the current radar jamming recognition method is difficult to extract artificial features and the recognition rate is not high in a strong noise environment, a radar active jamming recognition method combining long short-term memory (LSTM) network and residual network is proposed. The original time domain data of active suppression jamming is inputt, and a deep learning network model to is built classify and identify jamming signals under different jamming-to-noise tatio (JNR). The simulation results show that when the JNR is 0 dB, the recognition accuracy of the method for four types of radar active jamming signals is higher than 98.3%, which is compared with other deep learning algorithms such as pure residual network and CNN-LSTM, it has better network performance, which verifies the effectiveness of the algorithm.

Key words: jamming recognition, deep learning, long short-term memory (LSTM), residual network

中图分类号:

TN974

邵正途, 许登荣, 徐文利, 王晗中. 基于LSTM和残差网络的雷达有源干扰识别[J]. 系统工程与电子技术, 2023, 45(2): 416-423.

Zhengtu SHAO, Dengrong XU, Wenli XU, Hanzhong WANG. Radar active jamming recognition based on LSTM and residual network[J]. Systems Engineering and Electronics, 2023, 45(2): 416-423.

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网络层	关键参数	输出形状	激活函数
输入层	-	3 000×1	-
卷积层1	卷积核大小[100 1]核数目40, 步长100	30×1×40	RELU
卷积层2~卷积层7	卷积核大小[100 1]核数目40, 步长1, 填充方式“same”	30×1×40	RELU
最大池化层	池化核大小[20 1]	11×1×40	-
平坦层	-	440	-
LSTM层1	隐藏层数100	100	tanh, sigmoid
LSTM层2	隐藏层数30	30	tanh, sigmoid
全连接层	-	4	RELU
分类层	-	4	Softmax

信号	参数	取值范围
窄带干扰	中心频率	[1/5, 3/10]f_s
窄带干扰	干扰带宽	[1/10, 1/5]f_s
宽带干扰	中心频率	[1/5, 3/10] f_s
宽带干扰	干扰带宽	[1/5, 2/5]f_s
梳状谱干扰	谱线间隔	[1/20, 1/10]f_s
梳状谱干扰	干扰带宽	[1/5, 2/5]f_s
扫频干扰	扫频周期	[1/5, 1/3]τ_s
扫频干扰	扫频带宽	[1/5, 2/5]f_s

网络模型	空间复杂度/M	时间复杂度
网络模型	空间复杂度/M	每轮迭代耗时/s	平均识别耗时/ms
残差CNN-LSTM	1.19	2.37	0.28
CNN-LSTM	1.19	2.30	0.26
残差CNN	0.96	2.23	0.21

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