Unified Generative SAR Imaging for Multiple Ships With Distinct Dynamic States

Abstract

Synthetic aperture radar (SAR) possesses all-weather, all-time observation capabilities, offering broad potential in disaster monitoring, topographic surveying, and marine management. For maritime moving target imaging, the coupling of target maneuverability with the wave disturbance generates complex composite motion, posing significant challenges for imaging. Image defocusing caused by these motions can be compensated for by estimating motion parameters or phase errors. However, when dealing with multiple-ship scenarios, existing methods are limited to strategies that segment echoes or images and apply focusing processing on each target separately. This segmentation-based approach fails when dealing with targets in close proximity where their echoes or defocused side lobes overlap, and the targets cannot be focused simultaneously due to their distinct dynamic states. To address this challenge, a unified generative SAR imaging method for multiple moving targets with distinct dynamic states is proposed in this article. Under the constraint of the observed echoes, the method introduces a diffusion model to learn the data distribution of high-resolution SAR imaging results of multiple ship targets. Guided by constraints through an iterative inverse process, high-quality, clean imaging results are reconstructed. Experiments validate the effectiveness of the proposed method and its adaptability to different systems, target types, and imaging scenarios.