Self-supervised Learning Method for SAR Interference Suppression Based on Abnormal Texture Perception

Abstract

Facing the increasingly complex electromagnetic interference environment, Synthetic Aperture Radar (SAR) interference suppression has become an urgent problem to be solved. The existing mainstream synthetic aperture radar nonparametric/parametric interference suppression methods, which heavily rely on interference priori and strong energy difference, have serious problems such as high computational complexity and signal loss, and have difficulty in meeting the needs of countering increasingly complex interference. To solve the aforementioned problems, we propose an anti-interference method using self-supervised learning based on deep learning, which uses the time-frequency domain texture difference between normal radar echo and interference to overcome the constraint of using interference prior. First, we construct an interference location network model Location-Net, which compresses and reconstructs the time-frequency spectrum of the radar echo and locates the interference according to the network’s reconstruction error. Second, aiming at the signal loss caused by interference suppression, a signal recovery neural network model Recovery-Net is constructed to recover the echo signal after interference suppression. Compared with traditional methods, our method overcomes the need for interference prior, can effectively resist various complex interference types and has strong generalization ability. The anti-interference processing results based on simulation and measured data verify the effectiveness of the proposed method for various active main lobe suppression interference and show the superiority of the algorithm proposed here by comparing it with three existing anti-interference methods. Finally, comparing the complexity difference between the proposed and mainstream lightweight neural networks shows that the neural networks designed here have low computational complexity and real-time application prospects.

Keywords

• interference suppression
• signal recovery
• synthetic aperture radar (sar)
• deep learning
• self-supervised learning