Enhancing Target Imaging via Joint Sparse and Low-Rank Priors Using Real Data in Synthetic Aperture Interferometric Radiometer

In synthetic aperture interferometric radiometer (SAIR), compressive sensing (CS)-based imaging methods significantly improve reconstruction quality while requiring substantially fewer measurement constraints, thereby reducing hardware complexity for target detection applications. Despite the CS promising capabilities, the imaging quality is still limited in scenes with fewer visibility functions. The main reason is the elevated side-lobe level of the array factor, which results from the limited number of u-v baselines. To address this challenge, we propose a target imaging method via joint sparse and low-rank (JSLR) priors. The proposed method combines the target imaging framework of compressed interferometric radiometer (CIR) with matrix completion (MC) to enhance imaging quality. In the spatial domain, the target demonstrates sparsity, while in the spatial-frequency domain, it exhibits low-rank properties. Then, the MC method is employed to recover unobserved visibility functions. By combining sparsity and low-rank priors, we formulate the JSLR model. Finally, we introduce a reconstruction algorithm, known as the alternating direction method of multipliers (ADMM), to effectively solve the JSLR model. Results from simulations and real aircraft experiments demonstrate the superiority of the proposed JSLR method in enhancing imaging quality and stability.