Fast and Robust LRSD-based SAR/ISAR Imaging and Decomposition

TL;DR

This paper introduces a fast, robust LRSD-based SAR/ISAR imaging framework that effectively handles platform residual phase errors and reduces computational costs, improving image quality and processing speed.

Contribution

The proposed unified algorithm combines low-rank and sparse decomposition with recent quadratic programming techniques to enhance SAR/ISAR imaging and autofocusing while addressing phase errors and computational efficiency.

Findings

Outperforms state-of-the-art methods in image quality.

Reduces computational complexity significantly.

Effective in handling platform residual phase errors.

Abstract

The earlier works in the context of low-rank-sparse-decomposition (LRSD)-driven stationary synthetic aperture radar (SAR) imaging have shown significant improvement in the reconstruction-decomposition process. Neither of the proposed frameworks, however, can achieve satisfactory performance when facing a platform residual phase error (PRPE) arising from the instability of airborne platforms. More importantly, in spite of the significance of real-time processing requirements in remote sensing applications, these prior works have only focused on enhancing the quality of the formed image, not reducing the computational burden. To address these two concerns, this article presents a fast and unified joint SAR imaging framework where the dominant sparse objects and low-rank features of the image background are decomposed and enhanced through a robust LRSD. In particular, our unified algorithm circumvents the tedious task of computing the inverse of large matrices for image formation and takes advantage of the recent advances in constrained quadratic programming to handle the unimodular constraint imposed due to the PRPE. Furthermore, we extend our approach to ISAR autofocusing and imaging. Specifically, due to the intrinsic sparsity of ISAR images, the LRSD framework is essentially tasked with the recovery of a sparse image. Several experiments based on synthetic and real data are presented to validate the superiority of the proposed method in terms of imaging quality and computational cost compared to the state-of-the-art methods.