MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com Graph-Based Array Signal Denoising for Perturbed Synthetic Aperture Radar Liu, Dehong; Chen, Siheng; Boufounos, Petros T. TR2020-114 July 22, 2020 Abstract The performance of synthetic aperture radar degrades when its moving platform is per- turbed with unknown position errors or received signals are interfered by strong random noise. Therefore, it is desirable to perform robust imaging with noisy radar echoes even un- der large position perturbations. In this paper, we propose a graph-based denoising method, which regularizes both the smoothness in the graph domain and the sparse gradients in the time domain. Diﬀerent from previous GSP-based methods, our graph model is built in the radar signal domain instead of the image domain, so that we can jointly estimate position per- turbations of the radar platform and denoise the received signals, providing focused imaging results. Simulation results demonstrate that our method improves the radar imaging quality from 13.3dB provided by coherence analysis to 21.6dB in terms of PSNR. IEEE International Geoscience and Remote Sensing Symposium (IGARSS) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonproﬁt educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c  Mitsubishi Electric Research Laboratories, Inc., 2020 201 Broadway, Cambridge, Massachusetts 02139