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Study on Neural Network-Guided Sparse Recovery for Interrupted-Sampling Repeater Jamming Suppression | Chapter 14 | Novel Perspectives of Engineering Research Vol. 8

 ISRJ (interrupted-sampling repeater jamming) is a novel sort of DRFM-based jamming for linear frequency modulation (LFM) transmissions. ISRJ can obtain radar coherent processing gain by intercepting the radar signal slice and retransmitting it numerous times, resulting in the formation of multiple false target groups following pulse compression (PC), posing significant risks to radar imaging and target detection. However, in the time-frequency (TF) domain, the features of an ISRJ fragment interception can be exploited to discriminate from the true target signal. This study investigates the feasibility of exploiting the discontinuous distribution characteristics of ISRJ in the TF domain relative to the real target to create an adaptive interference suppression approach using a neural network. This work proposes a new method for ISRJ suppression based on the distribution characteristic of the echo signal and the coherence of ISRJ to radar signal. The position of the real target is calculated using a gated recurrent unit neural network (GRU-Net) in this method, and the real target may therefore be reconstructed by adaptive filtering in the sparse representation of the echo signal based on the target locating result. The reconstruction result comprises only the true target, and ISRJ's bogus target groups are totally suppressed. The projected GRU-Net has a target locating accuracy of up to. Simulations have demonstrated the efficacy of the proposed strategy.


Author(S) Details

Zijian Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China and School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.

Wenbo Yu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China and School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.

Zhongjun Yu
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China and School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.

Yunhua Luo
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.

Jiamu Li
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China and School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 101408, China.

View Book:- https://stm.bookpi.org/NPER-V8/article/view/6120

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