Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging

2015/07/07 by Huiping Duan, Lizao Zhang, Jun Fang +2 · 2 citations
Engineering · #Advanced SAR Imaging Techniques #Microwave Imaging and Scattering Analysis #Sparse and Compressive Sensing Techniques

paper · doi:10.1109/lsp.2015.2452412

openalex publication_date 2015/07/07 · crossref created 2015/07/07 · crossref issued 2015/11/01 · crossref published 2015/11/01 · crossref published-print 2015/11/01 · crossref deposited 2022/01/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/17 · crossref indexed 2026/07/30

Abstract

We propose a pattern-coupled sparse Bayesian learning method for inverse synthetic aperture radar (ISAR) imaging by exploiting a block-sparse structure inherent in ISAR target images. A two-dimensional pattern-coupled hierarchical Gaussian prior is proposed to model the pattern dependencies among neighboring scatterers on the target scene. An expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Numerical results are provided to illustrate the effectiveness of the proposed algorithm.

Citations

• Sparsity-Driven Synthetic Aperture Radar Imaging: Reconstruction, autofocusing, moving targets, and compressed sensing

Cited by

• Enhancing ISAR Image Efficiently via Convolutional Reweighted l 1 Minimization
• Compressive Near-Field Wideband Channel Estimation for THz Extremely Large-scale MIMO Systems

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