Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks

JIANG Yilin; YIN Ziru; SONG Yu

doi:10.11999/JEIT210132

Volume 44 Issue 2

Feb. 2022

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(2): 718-725

Jiaao YU, Shirui PENG, Xiaokun CHEN, Youquan LI. Equivalent Circuit Method for Hexagonal Loop Composite Absorbing Material[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1873-1878. doi: 10.11999/JEIT171103

Citation:

JIANG Yilin, YIN Ziru, SONG Yu. Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks[J]. Journal of Electronics & Information Technology, 2022, 44(2): 718-725. doi: 10.11999/JEIT210132

Citation:

PDF( 1681 KB)

Low Probability of Intercept Radar Signal Detection Algorithm Based on Convolutional Neural Networks

doi: 10.11999/JEIT210132

1.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China
2.
Key Laboratory of Advanced Marine Communication and Information Technology, Ministry of Industry and Information Technology, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62071137)

Received Date: 2021-02-05
Rev Recd Date: 2021-07-18

Available Online: 2021-08-10

Publish Date: 2022-02-25

Abstract

Abstract

In order to solve the problem of radar intercepting receiver’s unsatisfactory detection effect on Low Probability of Intercept (LPI) radar signal, a method of LPI radar signal detection based on Convolutional Neural Networks (CNN) is proposed, which defines signal and noise by effective signal pulse width in intercepted signal. The similarity of the convolution kernel and the matched filter in the structure can improve the detection accuracy of the signal under the low SNR.A large number of analog data sets based on four typical LPI radar signals (Linear Frequency Modulation signal (LFM), NonLinear Frequency Modulation signal (NLFM), Binary Phase Shift Keying signal (BPSK), COSTAS frequency coded signal) and white noise signals are used for CNN model training. At the same time, a small amount of measured signals (Linear Frequency Modulation signal (LFM), Binary Phase Shift Keying signal (BPSK)) are added as verification set for adaptation, so as to match better the detection model of measured signals. Finally, the experimental results show that the proposed algorithm has a good detection effect in the case of low SNR, and has the ability to generalize the LPI radar signals under various modulation modes and different SNR.
- Signal detection,
- Low Probability of Intercept (LPI),
- Convolutional Neural Networks (CNN),
- Measured signal,
- Low signal-to-noise ratio

FullText(HTML)

References(15)

References

[1]	鲍庆龙, 王森, 潘嘉蒙, 等. 非合作雷达辐射源目标探测系统关键技术分析[J]. 电波科学学报, 2020, 35(4): 496–503. BAO Qinglong, WANG Sen, PAN Jiameng, et al. Key technology analysis of target detection system based on non-cooperative radar illuminator[J]. Chinese Journal of Radio Science, 2020, 35(4): 496–503.
[2]	李娜, 王珂, 李保珠. 低截获概率雷达信号检测方法的优化及应用[J]. 光学精密工程, 2014, 22(11): 3122–3128. Li Na, Wang Ke, Li Baozhu. Optimization and application of LPI radar signal detection method[J]. Optics and Precision Engineering, 2014, 22(11): 3122–3128.
[3]	LIU Yongjian, XIAO Peng, WU Hongchao, et al. LPI radar signal detection based on radial integration of Choi-Williams time-frequency image[J]. Journal of Systems Engineering and Electronics, 2015, 26(5): 973–981. doi: 10.1109/JSEE.2015.00106
[4]	朱文涛, 郑纪彬, 苏涛, 等. 线性调频连续波信号的检测和参数估计[J]. 电子与信息学报, 2013, 35(7): 1562–1568. doi: 10.3724/SP.J.1146.2012.01356 ZHU Wentao, ZHENG Jibing, SU Tao, et al. Detection and parameter estimation of linear frequency modulation continuous wave signal[J]. Journal of Electronics &Information Technology, 2013, 35(7): 1562–1568. doi: 10.3724/SP.J.1146.2012.01356
[5]	刘锋, 徐会法, 陶然. 基于FRFT的对称三角LFMCW信号检测与参数估计[J]. 电子与信息学报, 2011, 33(8): 1864–1870. LIU Feng, XU Huifa, and TAO Ran. Detection and parameter estimation of symmetrical triangular LFMCW signal based on fractional Fourier transform[J]. Journal of Electronics &Information Technology, 2011, 33(8): 1864–1870.
[6]	徐会法, 胡晓峰, 张明智. 基于FRFT的几种典型相位编码信号检测与参数估计[J]. 兵工学报, 2012, 33(9): 1048–1054. XU Huifa, HU Xiaofeng, and ZHANG Mingzhi. Detection and parameter estimation of polyphase code signals based on fractional Fourier transform[J]. Acta Armamentarii, 2012, 33(9): 1048–1054.
[7]	黄宇, 刘锋, 王泽众, 等. 基于周期FRFT的多分量LFMCW雷达信号分离[J]. 航空学报, 2013, 34(4): 846–854. HUANG Yu, LIU Feng, WANG Zezhong, et al. Periodic FRFT-based multi-component LFMCW radar signal separating[J]. Acta Aeronautica et Astronautica Sinica, 2013, 34(4): 846–854.
[8]	陈艳丽, 郭良浩, 宫在晓. 简明分数阶傅里叶变换及其对线性调频信号的检测和参数估计[J]. 声学学报, 2015, 40(6): 761–771. CHEN Yanli, GUO Lianghao, and GONG Zaixiao. The concise fractional Fourier transform and its application in detection and parameter estimation of the linear frequency-modulated signal[J]. Acta Acustica, 2015, 40(6): 761–771.
[9]	李硕, 李天昀. 短波信道下的跳频信号检测[J]. 电子学报, 2019, 47(3): 623–629. LI Shuo and LI Tianyun. Frequency hopping signal detection over the Short-Wave Channel[J]. Acta Electronica Sinica, 2019, 47(3): 623–629.
[10]	GHADIMI G, NOROUZI Y, BAYDERKHANI R, et al. Deep learning-based approach for low probability of intercept radar signal detection and classification[J]. Journal of Communications Technology and Electronics, 2020, 65(10): 1179–1191. doi: 10.1134/S1064226920100034
[11]	WANG Li, TANG Jun, and LIAO Qingmin. A study on radar target detection based on deep neural networks[J]. IEEE Sensors Letters, 2019, 3(3): 7000504.
[12]	WANG Chao, WANG Jian, and ZHANG Xudong. Automatic radar waveform recognition based on time-frequency analysis and convolutional neural network[C]. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing, New Orleans, USA, 2017.
[13]	黄宇涛, 普运伟, 吴海潇, 等. 基于栈式自编码机和模糊函数主脊的雷达辐射源信号识别[J]. 电波科学学报, 2020, 35(5): 689–698. HUANG Yutao, PU Yunwei, WU Haixiao, et al. Radar emitter signal recognition based on stackedauto-encoder and ambiguity function main ridge[J]. Chinese Journal of Radio Science, 2020, 35(5): 689–698.
[14]	徐雅楠, 刘宁波, 丁昊, 等. 利用CNN的海上目标探测背景分类方法[J]. 电子学报, 2019, 47(12): 2505–2514. XU Yanan, LIU Ningbo, DING Hao, et al. Background classification method for marine target detection based on CNN[J]. Acta Electronica Sinica, 2019, 47(12): 2505–2514.
[15]	赵文静, 刘畅, 刘文龙, 等. K分布海杂波背景下基于最大特征值的雷达信号检测算法[J]. 电子与信息学报, 2018, 40(9): 2235–2241. doi: 10.11999/JEIT171092 ZHAO Wenjing, LIU Chang, LIU Wenlong, et al. Maximum eigenvalue based radar signal detection method for K distribution sea clutter environment[J]. Journal of Electronics &Information Technology, 2018, 40(9): 2235–2241. doi: 10.11999/JEIT171092

Relative Articles

Supplements(0)

Cited By

Cited by

Periodical cited type(7)

1.	张聿远，闫文君，张立民. 基于多模态特征融合网络的空时分组码识别算法. 电子学报. 2023(02): 489-498 .
2.	张聿远，闫文君，林冲，姚成柱. 利用卷积-循环神经网络的串行序列空时分组码识别方法. 信号处理. 2021(01): 19-27 .
3.	张聿远，闫文君，张立民，张媛. 基于卷积神经网络的串行空时分组码盲识别算法. 系统工程与电子技术. 2021(11): 3360-3370 .
4.	闫文君，张聿远，凌青，于柯远，谭凯文，刘恒燕. 基于频域互相关序列与峰值检测的空频分组码盲识别算法. 系统工程与电子技术. 2021(12): 3709-3715 .
5.	张天骐，范聪聪，喻盛琪，赵健根. 基于JADE与特征提取的正交/非正交空时分组码盲识别. 系统工程与电子技术. 2020(04): 933-939 .
6.	方伟，闫文君，凌青，张立民. 传输损耗条件下基于循环平稳检测的空时分组码盲识别方法. 计算机与现代化. 2018(02): 6-11 .
7.	黄波，潘爽，李雪. 基于四阶循环平稳的STBC-OFDM信号盲识别. 信号处理. 2017(09): 1221-1229 .