TY - JOUR
T1 - Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging
AU - Duan, Huiping
AU - Zhang, Lizao
AU - Fang, Jun
AU - Huang, Lei
AU - Li, Hongbin
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/1
Y1 - 2015/11/1
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
UR - http://www.scopus.com/inward/record.url?scp=84937125887&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84937125887&partnerID=8YFLogxK
U2 - 10.1109/LSP.2015.2452412
DO - 10.1109/LSP.2015.2452412
M3 - Article
AN - SCOPUS:84937125887
SN - 1070-9908
VL - 22
SP - 1995
EP - 1999
JO - IEEE Signal Processing Letters
JF - IEEE Signal Processing Letters
IS - 11
M1 - 7147823
ER -