Bayesian Inverse Synthetic Aperture Radar Imaging

• Published in: IEEE geoscience and remote sensing letters 2011-11, Vol.8 (6), p.1150-1154
• Main Authors: Xu, Gang, Xing, Mengdao, Zhang, Lei, Liu, Yabo, Li, Yachao
• Format: Article
• Language: English
• Subjects: Algorithms; Bayesian; Bayesian analysis; Bayesian methods; denoising; Estimation; Fourier transforms; Imaging; Inverse synthetic aperture radar; inverse synthetic aperture radar (SAR) (ISAR); Mathematical models; Noise; Noise reduction; phase adjustment; Radar imaging; Signal to noise ratio; sparsity-driven optimization; Studies
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2011.2158797

In this letter, a novel algorithm of inverse synthetic aperture radar (ISAR) imaging based on Bayesian estimation is proposed, wherein the ISAR imaging joint with phase adjustment is mathematically transferred into signal reconstruction via maximum a posteriori estimation. In the scheme, phase errors are treated as model errors and are overcome in the sparsity-driven optimization regardless of the formats, while data-driven estimation of the statistical parameters for both noise and target is developed, which guarantees the high precision of image generation. Meanwhile, the fast Fourier transform is utilized to implement the solution to image formation, promoting its efficiency effectively. Due to the high denoising capability of the proposed algorithm, high-quality image also could be achieved even under strong noise. The experimental results using simulated and measured data confirm the validity.