中图分类号：TN957 文献标志码：A DOI: 10.16157/j.issn.0258-7998.256987
中文引用格式： 陈文瑞，刘方，戴广钊. 基于稀疏重构与深度学习的雷达抗有源干扰信号处理算法[J]. 电子技术应用，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