Fast Compressed Sensing SAR Imaging based on Approximated Observation

TL;DR

This paper introduces a fast compressed sensing SAR imaging method that uses an approximated observation model, significantly reducing computational costs while maintaining high-resolution imaging capabilities.

Contribution

It proposes a novel CS-SAR imaging model based on an approximated observation derived from inverse focusing, enabling efficient high-resolution SAR imaging at sub-Nyquist sampling rates.

Findings

Effective SAR imaging with reduced computational cost

Suitable for high-resolution, large-scale applications

Performs well with both simulated and real SAR data

Abstract

In recent years, compressed sensing (CS) has been applied in the field of synthetic aperture radar (SAR) imaging and shows great potential. The existing models are, however, based on application of the sensing matrix acquired by the exact observation functions. As a result, the corresponding reconstruction algorithms are much more time consuming than traditional matched filter (MF) based focusing methods, especially in high resolution and wide swath systems. In this paper, we formulate a new CS-SAR imaging model based on the use of the approximated SAR observation deducted from the inverse of focusing procedures. We incorporate CS and MF within an sparse regularization framework that is then solved by a fast iterative thresholding algorithm. The proposed model forms a new CS-SAR imaging method that can be applied to high-quality and high-resolution imaging under sub-Nyquist rate sampling, while saving the computational cost substantially both in time and memory. Simulations and real SAR data applications support that the proposed method can perform SAR imaging effectively and efficiently under Nyquist rate, especially for large scale applications.