Neural Network-Guided Sparse Recovery for Interrupted-Sampling Repeater Jamming Suppression

• Published in: International journal of antennas and propagation Vol. 2021; pp. 1 - 13
• Main Authors: Wang, Zijian, Yu, Wenbo, Yu, Zhongjun, Luo, Yunhua, Li, Jiamu
• Format: Journal Article
• Language: English
• Published: New York Hindawi 02.12.2021; Hindawi Limited
• Subjects: Adaptive filters; Deep learning; Frequency modulation; Jamming; Military communications; Neural networks; Parameter estimation; Pulse compression; Radar; Radar systems; Sampling; Signal processing
• ISSN: 1687-5869; 1687-5877
• DOI: 10.1155/2021/5368600

Interrupted-sampling repeater jamming (ISRJ) is a new type of DRFM-based jamming designed for linear frequency modulation (LFM) signals. By intercepting the radar signal slice and retransmitting it many times, ISRJ can obtain radar coherent processing gain so that multiple false target groups can be formed after pulse compression (PC). According to the distribution characteristic of the echo signal and the coherence of ISRJ to radar signal, a new method for ISRJ suppression is proposed in this study. In this method, the position of the real target is determined using a gated recurrent unit neural network (GRU-Net), and the real target can be, therefore, reconstructed by adaptive filtering in the sparse representation of the echo signal based on the target locating result. The reconstruction result contains only the real target, and the false target groups formed by ISRJ are suppressed completely. The target locating accuracy of the proposed GRU-Net can reach 92.75%. Simulations have proved the effectiveness of the proposed method.