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摘要:
针对低截获概率(LPI)雷达信号在低信噪比(SNR)情况下识别效果不理想、网络模型复杂的问题,提出一种基于时频重排和多尺度残差网络的LPI雷达信号识别方法。该方法以魏格纳-维尔分布(WVD)为基础,通过时频重排算法提高信号的聚集性,得到信号的时频分布图像,输入到多尺度残差网络中完成信号的分类。通过构建多径莱斯衰落信道完成复杂电磁环境仿真,实验结果表明:所提方法在SNR为−8 dB时,对Costas、Frank、P1~P4等13类LPI雷达典型调制样式能达到94%的识别准确率,相比其他方法在低信噪比下具有更好的识别性能。
Abstract:In view of the problems of low probability of acquisition (LPI) radar signal recognition in low signal-to-noise ratio (SNR) and complex network model, an LPI radar signal recognition method based on time-frequency reassignment and multi-scale residual network was proposed. The time-frequency reassignment approach is used to enhance the signal's aggregation based on the Wigner-Ville distribution (WVD). The resulting time-frequency distribution image is then fed into the multi-scale residual network to finish the signal's categorization. In addition, the complex electromagnetic environment simulation was completed by constructing a multi-path Rice-fading channel. According to the experimental results, when the SNR is −8 dB, the suggested approach can achieve 94% recognition accuracy for a total of 13 different types of typical LPI radar modulation patterns, including Costas, Frank, P1~P4, etc. Compared with other methods, it has better recognition performance at a low signal-to-noise ratio.
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表 1 波形参数设置
Table 1. Waveform parameters setting
LPI信号调制样式 调制参数 取值范围 All 载频fc/Hz U(fs /6, fs/5) 线性调频 带宽B/Hz
采样个数NU(fs/20,fs/15)
[512,1024 ]Rect 采样个数N [512, 1024 ]Costas 跳频序列长度
基础频率fmin/Hz
采样个数N{3,4,5,6}
U(fs/32, fs/25)
[512,1024 ]Barker 码长L
相位子波数cpp{7,11,13}
[2,5]Frank 相位子波数cpp
频率步长M[3,5]
{6,7,8}P1,P2 相位子波数cpp
频率步长M[3,5]
{6,8}P3,P4 相位子波数cpp
子码数ns[3,5]
{36,64}T1,T2 相位状态数Np
相位波形段数Ng
采样个数N2
{4,5,6}
[512,1024 ]T3,T4 相位状态数Np
相位波形段数Ng
采样个数N
调制带宽B/Hz2
{4,5,6}
[512,1024 ]
U(fs/20,fs/15)表 2 信道参数设置
Table 2. Channel parameters setting
信道参数 取值范围 采样频率/Hz fs 多径衰减/ns U(1, 1000 )平均路径增益/dB U(−20,0) K-factor 4 最大多普勒频移/Hz U(10, 1000 )表 3 运行时间对比
Table 3. Comparison of running time
时频方法 时间/s 本文方法 0.269 3 CWD 0.307 2 WVD 0.039 2 STFT 0.064 6 SPWVD 0.264 1 MSST 0.199 3 -
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