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

Abstract

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), which causes great threats to radar imaging and target detection. However, the characteristics of such fragment interception of ISRJ can be used to distinguish from the real target signal in the time-frequency (TF) domain. Based on this, this study explores the possibility of using the discontinuous distribution characteristics of ISRJ in the TF domain relative to the real target to implement adaptive interference suppression method with the help of neural network. 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 . Simulations have proved the effectiveness of the proposed method.