Refocusing of SAR Ground Moving Target Based on Generative Adversarial Networks

Wen Tang, Jiang Qian, Lu Wang, Yong Wang

Abstract: Due to the range and azimuth velocity of moving targets, severe defocusing occurs in synthetic aperture radar (SAR) images. The traditional ground moving target imaging algorithm generally needs to estimate the parameters of the moving target, and then conduct the refocusing of the moving target according to the estimated parameters. In this paper, a SAR moving target refocusing algorithm based on generative adversarial network (GAN) is proposed without estimating the motion parameters of the targets. To get a sufficiently trained network, we propose to use simulated moving target data to train the model and evaluate its performance using real data. The results of numerical experiments show that the trained network using simulated data can be well transferred to real test data and effectively achieve to refocus multiple moving targets with distinct velocities at the circumstance of heavy noise.

External IDs:dblp:conf/igarss/TangQWW22