Research on SAR active jammer-resistant imaging based on sparse reconstruction

Advances in artificial intelligence and intelligent algorithms have opened new avenues for radar signal processing. To address SAR imaging degradation caused by active interference, this study proposes an anti-interference imaging model based on sparse reconstruction. By integrating sparse representation and regularization optimization into the traditional SAR imaging framework, the model achieves sparse recovery of target signals and separation of interference energy through joint time-frequency filtering and iterative thresholding algorithms. Under JNR=25 dB conditions, the signal-to-noise ratio improves by approximately 8.4 dB, PSNR increases to 31.8 dB, and image structural similarity remains above 0.91. Results demonstrate that this method exhibits higher imaging accuracy and stability under both strong interference and low signal-to-noise environments.