Image Reconstruction for Low-Oversampled Staggered SAR Based on Bayesian Compressive Sensing

Staggered synthetic aperture radar (SAR) is an innovative concept of high-resolution and wide-swath systems, it combines SCan-On-REceive (SCORE) with continuous variation of the pulse repetition interval (PRI) to deal with the blind ranges over wide areas. Since the acquired data is not uniformly sampled and is partially lost, proper reconstruction methods should be considered to achieve good image quality. The existing reconstruction algorithms mostly resample the nonuniformly sampled signal into a uniform grid and then perform traditionally focused processing. However, these algorithms lack robustness with regard to the PRI strategies, especially under low oversampling or even sub-Nyquist sampling. In this article, a reconstruction algorithm based on Bayesian compressive sensing (BCS) is proposed for low-oversampled staggered (LS) SAR in the presence of an optimum random strategy. Simulations and experiments on raw data generated in LS SAR are performed to verify the effectiveness of the proposed method.