基于时频重排算法的LPI雷达信号识别

姬丽彬; 朱岩; 崔天舒; 王栋; 黄永辉

doi:10.13700/j.bh.1001-5965.2023.0218

基于时频重排算法的LPI雷达信号识别

doi: 10.13700/j.bh.1001-5965.2023.0218

姬丽彬^{1, 2},
朱岩^1, ,,
崔天舒³,
王栋^{1, 2},
黄永辉¹

1.
中国科学院国家空间科学中心复杂航天系统电子信息技术重点实验室，北京 100190
2.
中国科学院大学计算机科学与技术学院，北京 100049
3.
清华大学北京信息科学与技术国家研究中心，北京 100084

基金项目: 中国科学院复杂航天系统电子信息技术重点实验室自主部署基金(Y42613A32S)

详细信息

通讯作者:
E-mail：zhuyan@nssc.ac.cn

中图分类号: TN974
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- 被引次数: 0
出版历程
- 收稿日期: 2023-04-27
- 录用日期: 2023-07-21
- 网络出版日期: 2023-08-01
- 整期出版日期: 2025-04-30

LPI radar signal recognition based on time-frequency reassignment algorithm

JI Libin^{1, 2},
ZHU Yan^{1
, ,},
CUI Tianshu³,
WANG Dong^{1, 2},
HUANG Yonghui¹

1.
Key Laboratory of Electronic and Information Technology for Complex Space Systems，National Space Science Center，Chinese Academy of Science，Beijing100190，China
2.
School of Computer Science and Technology，University of Chinese Academy of Science，Beijing 100049，China
3.
Beijing National Research Center for Information Science and Technology，Tsinghua University，Beijing 100084，China

Funds: Independent Deployment Foundation of Key Laboratory of Electronic Information Technology for Complex Space Systems, Chinese Academy of Sciences (Y42613A32S)

More Information

Corresponding author: E-mail：zhuyan@nssc.ac.cn

摘要

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。
- 低截获概率雷达 /
- 信号识别 /
- 时频重排 /
- 多尺度残差网络 /
- 调制样式
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.
- low intercept probability radar /
- signal recognition /
- time-frequency reassignment /
- multi-scale residual network /
- modulation style

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图 1 SNR为10 dB时13种雷达信号的RSPWVD时频图像

Figure 1. RSPWVD time-frequency images of 13 kinds of radar signals with SNR of 10 dB

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图 2 图像预处理流程

Figure 2. Flow chart of image preprocessing

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图 3 多尺度残差网络模型

Figure 3. Multi-scale residual network model

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图 4 LPI雷达信号调制识别流程

Figure 4. LPI radar signal modulation recognition process

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图 5 13种雷达调制方式的识别准确率

Figure 5. Recognition accuracy of 13 radar modulation modes

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图 6 信号的混淆矩阵（−8 dB）

Figure 6. Signal confusion matrix (−8 dB)

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图 7 6种方法识别准确率对比

Figure 7. Comparison of recognition accuracy of six methods

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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f_c/Hz	U(f_s /6, f_s/5)
线性调频	带宽B/Hz 采样个数N	U(f_s/20,f_s/15) [512，1024]
Rect	采样个数N	[512，1024]
Costas	跳频序列长度基础频率f_min/Hz 采样个数N	{3，4，5，6} U(f_s/32, f_s/25) [512，1024]
Barker	码长L 相位子波数c_pp	{7，11，13} [2，5]
Frank	相位子波数c_pp 频率步长M	[3，5] {6，7，8}
P1,P2	相位子波数c_pp 频率步长M	[3，5] {6，8}
P3,P4	相位子波数c_pp 子码数n_s	[3，5] {36，64}
T1,T2	相位状态数N_p 相位波形段数N_g 采样个数N	2 {4，5，6} [512，1024]
T3,T4	相位状态数N_p 相位波形段数N_g 采样个数N 调制带宽B/Hz	2 {4，5，6} [512，1024] U(f_s/20,f_s/15)

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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	f_s
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)

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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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