中圖分類號：TN957 文獻標志碼：A DOI: 10.16157/j.issn.0258-7998.256987
中文引用格式： 陳文瑞，劉方，戴廣釗. 基于稀疏重構與深度學習的雷達抗有源干擾信號處理算法[J]. 電子技術應用，2026，52(4)：134-138.
英文引用格式： Chen Wenrui，Liu Fang，Dai Guangzhao. Radar anti-active jamming signal processing algorithm based on sparse reconstruction and deep learning[J]. Application of Electronic Technique，2026，52(4)：134-138.

Radar anti-active jamming signal processing algorithm based on sparse reconstruction and deep learning

Abstract： In order to solve the problem of digital RF signal duplication interference and broadband noise interference threatening radar target detection, this paper proposes an interference suppression algorithm that combines sparse reconstruction and deep learning. The algorithm first uses deep learning to extract interference features and detect the interference type and location, and then adopts a space-time adaptive processing strategy based on sparse representation to suppress interference in the space and time domains. Simulation experiments show that compared with traditional methods, this algorithm significantly improves the target detection probability and output signal-to-interference-noise ratio under strong interference background, and can effectively suppress main lobe simulation interference and broadband noise interference, providing a new idea for radar interference suppression and having engineering application value.

Key words : active signal interference；space-time adaptive processing；sparse reconstruction；deep learning