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

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

doi:10.1109/LSP.2015.2452412

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

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

Stevens Institute of Technology

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	7147823
Pages (from-to)	1995-1999
Number of pages	5
Journal	IEEE Signal Processing Letters
Volume	22
Issue number	11
DOIs	https://doi.org/10.1109/LSP.2015.2452412
State	Published - Nov 1 2015

ASJC Scopus subject areas

Signal Processing
Electrical and Electronic Engineering
Applied Mathematics

Keywords

Block-sparse structure
ISAR
expectation-maximization (EM)
pattern-coupled sparse bayesian learning

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10.1109/LSP.2015.2452412

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title = "Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging",

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 = "Block-sparse structure, ISAR, expectation-maximization (EM), pattern-coupled sparse bayesian learning",

author = "Huiping Duan and Lizao Zhang and Jun Fang and Lei Huang and Hongbin Li",

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doi = "10.1109/LSP.2015.2452412",

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AU - Duan, Huiping

AU - Zhang, Lizao

AU - Fang, Jun

AU - Huang, Lei

AU - Li, Hongbin

PY - 2015/11/1

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N2 - 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.

AB - 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.

KW - Block-sparse structure

KW - ISAR

KW - expectation-maximization (EM)

KW - pattern-coupled sparse bayesian learning

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DO - 10.1109/LSP.2015.2452412

M3 - Article

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SP - 1995

EP - 1999

JO - IEEE Signal Processing Letters

JF - IEEE Signal Processing Letters

IS - 11

M1 - 7147823

ER -

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

Abstract

ASJC Scopus subject areas

Keywords

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