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

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

Research output: Contribution to journalArticlepeer-review

85 Scopus citations

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 languageEnglish
Article number7147823
Pages (from-to)1995-1999
Number of pages5
JournalIEEE Signal Processing Letters
Volume22
Issue number11
DOIs
StatePublished - 1 Nov 2015

Keywords

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

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