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

Abstract

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

CLC Number:

TN971

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.

Figures/Tables 11

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References 30

1	熊坤来. 盲分离及其在阵列信号处理中的应用研究[D]. 长沙: 国防科学技术大学, 2015.
	XIONG K L. Reaserch on blind separation and its application in array signal processing[D]. Changsha: National University of Defense Technology, 2015.
2	ANSARI S , ALATRANY A S , ALNAJJAR K A , et al. A survey of artificial intelligence approaches in blind source separation[J]. Neurocomputing, 2023, 561 (1): 126895- 126920.
3	CARDOSO J F , SOULOUMIAC A . Blind beamforming for non-Gaussian signals[J]. IEE Proceedings F: Radar and Signal Processing, 1993, 140 (6): 362- 370. doi: 10.1049/ip-f-2.1993.0054
4	CICHOCKI A , UNBEHAUEN R . Robust neural networks with on-line learning for blind identification and blind separation of sources[J]. IEEE Trans. on Circuits and Systems Ⅰ: Fundamental Theory and Applications, 1996, 43 (11): 894- 906.
5	HYVARINEN A , OJA E . A fast fixed-point algorithm for independent component analysis[J]. Neural Computation, 1997, 9 (7): 1483- 1492. doi: 10.1162/neco.1997.9.7.1483
6	GU J J , YAO D D , LI J F , et al. A novel semi-blind source sepa- ration framework towards maximum signal-to-interference ratio[J]. Signal Processing, 2024, 217, 109359. doi: 10.1016/j.sigpro.2023.109359
7	CELIK H , KARABOGA N . Blind source separation with strength Pareto evolutionary algorithm 2 (SPEA2) using discrete wavelet transform[J]. Electronics, 2023, 12 (21): 4383- 4395. doi: 10.3390/electronics12214383
8	TANG M Y , WU Y F . A blind source separation method based on bounded component analysis optimized by the improved beetle antenna search[J]. Sensors, 2023, 23 (19): 8325- 8345. doi: 10.3390/s23198325
9	付卫红, 周雨菲, 赵文胜. 面向能量差异混合信号的单通道盲源分离算法[J]. 系统工程与电子技术, 2023, 45 (10): 3302- 3311. doi: 10.12305/j.issn.1001-506X.2023.10.35
	FU W H , ZHOU Y F , ZHAO W S . Single-channel blind source separation algorithm for energy differential mixed signals[J]. Systems Engineering and Electronics, 2023, 45 (10): 3302- 3311. doi: 10.12305/j.issn.1001-506X.2023.10.35
10	骆伟林, 金宏斌, 李浩, 等. 基于混沌自适应烟花算法的雷达信号盲源分离[J]. 系统工程与电子技术, 2020, 42 (11): 2497- 2505. doi: 10.3969/j.issn.1001-506X.2020.11.11
	LUO W L , JIN H B , LI H , et al. Blind source separation of radar signals based on chaotic adaptive firework algorithm[J]. Systems Engineering and Electronics, 2020, 42 (11): 2497- 2505. doi: 10.3969/j.issn.1001-506X.2020.11.11
11	RI J G , JONG S H , KIM W C , et al. An algorithm for mixing matrix estimation in underdetermined blind source separation[J]. Circuits, Systems, and Signal Processing, 2022, 41 (9): 5049- 5064. doi: 10.1007/s00034-022-02020-8
12	LI Y Y , ATHIAR D R . Research on mixed matrix estimation algorithm based on improved sparse representation model in underdetermined blind source separation system[J]. Electronics, 2023, 12 (2): 456- 476. doi: 10.3390/electronics12020456
13	DU Y C , SCHEIBLER R , TOGAMI M , et al. Computationally- efficient overdetermined blind source separation based on iterative source steering[J]. IEEE Signal Processing Letters, 2022, 29 (1): 927- 931.
14	WANG Y P , ZHANG Q S , CAO R F , et al. The single-channel blind source separation based on VMD and Tukey M estimation for rolling bearing composite fault diagnosis[J]. Measurement and Control, 2023, 56 (9): 1599- 1612.
15	WANG G , WANG Y B , MIN Y Z , et al. Blind source separation of transformer acoustic signal based on sparse component analysis[J]. Energies, 2022, 15 (16): 6017- 6031. doi: 10.3390/en15166017
16	CHEN S , YUAN Y , WANG S X , et al. Multi-electromagnetic jamming countermeasure for airborne SAR based on maximum SNR blind source separation[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5240211.
17	CHEN S , LIN Y , YUAN Y , et al. Airborne SAR suppression of blanket jamming based on second order blind identification and fractional order Fourier transform[J]. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5212014.
18	DAI Z, GU H. 2D DOA estimation of coherently distributed sources for planar arrays based on blind signal separation[C]//Proc. of the 7th International Conference on Computer-Aided Design, Manufacturing, Modeling and Simulation, 2021, 1802: 042005.
19	LIN J, JULIANO J, ERDOGAN A, et al. Pulse separation using independent component analysis[C]//Proc. of the 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, 2020: 946-950.
20	HOU C B , LIU G W , TIAN Q , et al. Multisignal modulation classification using sliding window detection and complex convo-lutional network in frequency domain[J]. IEEE Internet of Things Journal, 2022, 9 (19): 19438- 19449. doi: 10.1109/JIOT.2022.3167107
21	QU Z Y , HOU C F , HOU C B , et al. Radar signal intra-pulse modulation recognition based on convolutional neural network and deep q-learning network[J]. IEEE Access, 2020, 8, 49125- 49136. doi: 10.1109/ACCESS.2020.2980363
22	LUO W L , YANG R J , JIN H B , et al. Single channel blind source separation of complex signals based on spatial-temporal fusion deep learning[J]. IET Radar, Sonar & Navigation, 2022, 17 (2): 200- 211.
23	JIN H B , LUO W L , LI H , et al. Underdetermined blind source separation of radar signals based on genetic annealing algorithm[J]. The Journal of Engineering, 2022, 2022 (3): 261- 273. doi: 10.1049/tje2.12108
24	XIAO Y C , ZHU F , ZHUANG S X , et al. Blind source separation and deep feature learning network-based identification of multiple electromagnetic radiation sources[J]. IEEE Trans. on Instrumentation and Measurement, 2023, 72, 2508813.
25	CHEN Y L , SHAO J , ZHAO Y H , et al. An improved separation method of multi-components signal for sensing based on time-frequency representation[J]. The Review of Scientific Instruments, 2019, 90 (6): 064901. doi: 10.1063/1.5082776
26	LI D W, WU M H, YU L, et al. Single-channel blind source separation of underwater acoustic signals using improved NMF and FastICA[EB/OL]. [2024-02-04]. https://doi.org/10.3389/fmars.2022.1097003.
27	MA H , ZHEN X , WU X R , et al. A blind separation algorithm for underdetermined convolutional mixed communication signals based on time-frequency soft mask[J]. Physical Communication, 2022, 53 (1): 101747- 101756.
28	SHEEJA J J , SANKARAGOMATHI B . CNN-QTLBO: an optimal blind source separation and blind dereverberation scheme using lightweight CNN-QTLBO and PCDP-LDA for speech mixtures[J]. Signal, Image and Video Processing, 2022, 16, 1323- 1331. doi: 10.1007/s11760-021-02084-0
29	LAILA G, CHRISTOPH H, EUGENIJUS K, et al. Estimation of central blood pressure waveform from femoral blood pressure waveform by blind sources separation[EB/OL]. [2024-02-04]. https://doi.org/10.3389/fcvm.2023.1280899.
30	MASSAR H , NSIRI B , TAOUFIQ D B . DWT-BSS: blind source separation applied to EEG signals by extracting wavelet transform's approximation coefficients[J]. Journal of Physics: Conference Series, 2023, 2550 (1): 012031. doi: 10.1088/1742-6596/2550/1/012031

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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Radar multi-component signal recognition method based on blind source separation combined with singular spectrum analysis

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