DNN With Similarity Constraint for GEO SA-BSAR Moving Target Imaging

Abstract

A GEOsynchronous Spaceborne-Airborne Bistatic Synthetic Aperture Radar (GEO SA-BSAR) system has been proved to be a significant tool for moving targets monitoring. Due to the special geometry model of the GEO SA-BSAR system, there is a complex relative movement between the moving target and the bistatic radar, leading to an additional phase modulation of the echo and further, causing moving targets to be smeared in the SAR image. Recently, Deep Neural Network (DNN) shows great potential in rapid image recovery. However, most image recovery methods based on DNN concentrate on the whole image, which limits the imaging performance of sparse targets. In this letter, we propose a DNN framework with similarity constraints for GEO SA-BSAR moving target imaging. This DNN-based method optimizes the cosine similarity of azimuth signals between the ground-truth image and the predicted image in the loss function to recover the azimuth position and focusing characteristics of the sparse targets. Extensive experimental results prove that the proposed model can quickly obtain GEO SA-BSAR moving target images with small training datasets compared with some counterparts.