A New Method of Robust Ground Moving Target Detection Under Different Backgrounds of Airborne SAR Based on Spatial Deformable Module

Abstract

Combined with the synthetic aperture radar (SAR) imaging algorithm, diverse simulation sample sets of ground moving targets are constructed to tackle the problem of insufficient measured data in the SAR ground moving target indication algorithm based on deep learning. In view of this, a overall scheme of realizing robust detection of ground moving targets under varying backgrounds is conducted on the integration of the adaptive spatial location extraction network based on deformable module and the multichannel clutter suppression technology. In particular, a spatial deformable module is incorporated into the network to enhance its modeling capacity of the input targets with different shapes. Furthermore, the multichannel clutter suppression technology of airborne SAR is adopted to significantly mitigate the interference of complex background clutter. The effectiveness of the proposed method is verified on the simulation sample sets, and comparison with other detection methods is given simultaneously.

Keywords

• Adaptive spatial location extraction network based on deformable module (ASLE-DM)
• clutter suppression
• ground moving target detection
• synthetic aperture radar (SAR) imaging algorithm