Bistatic inverse synthetic aperture radar sparse aperture self-focusing algorithm based on the joint constraint of compressed sensing and minimum Tsallias entropy

• Published in: Journal of applied remote sensing Vol. 16; no. 3; p. 036504
• Main Authors: Zhu, Hanshen, Guo, Baofeng, Hu, Wenhua, Jiao, Liting, Zhu, Xiaoxiu, Xue, Dongfang, Zhu, Chang’an
• Format: Journal Article
• Language: English
• Published: Society of Photo-Optical Instrumentation Engineers 01.07.2022
• Subjects: sparse aperture; fast sparse Bayesian learning; self-focusing; Bistatic inverse synthetic aperture radar; Tsallis entropy
• ISSN: 1931-3195; 1931-3195
• DOI: 10.1117/1.JRS.16.036504

Based on the low imaging resolution of bistatic inverse synthetic aperture radar (Bi-ISAR) and the failure of pulse correlation under the condition of sparse aperture cause that of the traditional self-focusing algorithm, a Bi-ISAR sparse aperture self-focusing algorithm with the combined constraint of image quality optimization and sparsity is proposed. First, the proposed algorithm establishes the Bi-ISAR sparse aperture self-focusing signal model, reconstructs images through fast sparse Bayesian learning (FSBL), uses the minimum Tsallis entropy and constraints the reconstruction process, iteratively updates the phase error, and performs self-focusing to realize the initial phase correction of Bi-ISAR images. Simulation results show that the proposed algorithm has a fast convergence speed, strong robustness to noise, and high accuracy in reconstructing images.