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LIAO Zhipeng, DUAN Keqing, and GAO Fei. Collaborative nonlinear space-time adaptive processing and pulse compression based on neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR25096
Citation: LIAO Zhipeng, DUAN Keqing, and GAO Fei. Collaborative nonlinear space-time adaptive processing and pulse compression based on neural networks[J]. Journal of Radars, in press. doi: 10.12000/JR25096
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Collaborative Nonlinear Space-time Adaptive Processing and Pulse Compression Based on Neural Networks

DOI: 10.12000/JR25096 CSTR: 32380.14.JR25096
  • 1.

    School of Electronic and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China

  • 2.

    China Academy Space Technology Xian Branch, Xi’an 710100, China

Funds:  The Foundation of National Key Laboratory of Radar Signal Processing (JKW202302)
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  • Corresponding author: DUAN Keqing, duankeqing@aliyun.com
  • Received Date: 2025-05-23
  • Rev Recd Date: 2025-07-28
    • Available Online: 2025-08-04
    Abstract
  • Traditional airborne radar Pulse Compression (PC) and Space-Time Adaptive Processing (STAP) suffer performance degradation in complex target and clutter environments due to their reliance on predefined linear models. To address this issue, we developed a deep learning-based joint STAP-PC technique. This approach employed dedicated networks—a super-resolution space-time spectrum network for nonlinear clutter estimation and a PC network for nonlinear PC. The proposed architecture effectively mitigated model mismatch within the processing chain, leading to improved clutter suppression and target detection. Notably, we mathematically established the feasibility of post-pulse compensation to prevent nonlinear PC from introducing phase errors across elements and pulses. The implemented architecture utilized multimodule convolutional neural networks for super-resolution space-time spectrum estimation and PC, with each module’s functionality demonstrating clear mathematical correspondence, thereby ensuring the reliability of the overall processing chain. Simulation results revealed that in scenarios with dense weak targets and limited samples, the proposed nonlinear joint processing technique improved signal-to-clutter-plus-noise ratio by approximately 20 dB over traditional methods.

     

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