自监督特征相似度表征的恒虚警检测方法

doi:10.12305/j.issn.1001-506X.2025.12.02

Abstract

Abstract:

For the problem of weak target detection, the detection performance of the constant false alarm rate （CFAR） algorithm based on power parameter features is limited by the signal-to-noise ratio （SNR） of the target echo signal. How to mine richer features beyond power parameters to characterize the target and achieve weak target detection at longer distances is the focus. In regard to this, a CFAR detection method based on self-supervised feature similarity representation is proposed. A dual-port deep network feature extraction model is constructed to fully mine the features of fragmented signals and their own rotated signals and represent them with similarity parameters. By maximizing the spatial distance of similarity parameters between the target space and the target-free space, the optimal feature extraction and parameter representation model is obtained. Finally, the similarity parameter detection threshold under the CFAR condition is calculated through numerical statistics. The results of processing simulation data and radar measured data in multiple frequency bands show that, with the requirement of CFAR, compared to the CFAR detection algorithm and various big data processing algorithms, the proposed method has higher generalization ability to unknown echo signal parameters and unknown background noise distribution characteristics, with an equivalent SNR improvement of 3 dB, has higher detection performance.

Key words: weak target detection, constant false alarm rate （CFAR） detection, similarity representation, radar signal processing

CLC Number:

TN 958

Houhong XIANG, Hongwei MA, Haijun YU, Yufeng CHEN, Fengyu WANG, Xiaolu ZENG. Constant false alarm rate detection method for self-supervised feature similarity representation[J]. Systems Engineering and Electronics, 2025, 47(12): 4012-4023.

Figures/Tables 13

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窗长l₀	p_fa
窗长l₀	10⁻¹	10⁻²	10⁻³	10⁻⁴	10⁻⁵	10⁻⁶
9	90.79	15.64	0.45	0.07	0.02	0.01
17	188.70	66.19	27.61	10.92	0.46	0.06
25	270.96	84.79	26.06	8.13	1.95	0.11
33	259.99	82.76	25.64	7.30	2.17	0.08

虚警概率p_fa	检测门限
10⁻¹	55.24
10⁻²	19.94
10⁻³	6.26
10⁻⁴	1.26

虚警概率p_fa	检测门限
10⁻¹	50.17
10⁻²	17.02
10⁻³	7.15
10⁻⁴	3.85

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Constant false alarm rate detection method for self-supervised feature similarity representation

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