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

Author

Huiping Duan ; Lizao Zhang ; Jun Fang ; Lei Huang ; Hongbin Li

Author_Institution

Sch. of Electron. Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China

Volume

22

Issue

11

fYear

2015

fDate

Nov. 2015

Firstpage

1995

Lastpage

1999

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.

Keywords

Gaussian processes; expectation-maximisation algorithm; learning (artificial intelligence); radar computing; radar imaging; synthetic aperture radar; 2D pattern-coupled hierarchical Gaussian prior; EM algorithm; ISAR imaging; MAP estimate; block-sparse structure; expectation-maximization algorithm; hyperparameters; inverse synthetic aperture radar imaging; maximum-a-posterior estimate; pattern-coupled sparse Bayesian learning method; Bayes methods; Covariance matrices; Electronic mail; Imaging; Radar imaging; Scattering; Signal processing algorithms; Block-sparse structure; ISAR; expectation-maximization (EM); pattern-coupled sparse bayesian learning;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2015.2452412

Filename

7147823