DocumentCode :
3605884
Title :
High-Resolution Passive SAR Imaging Exploiting Structured Bayesian Compressive Sensing
Author :
Qisong Wu ; Zhang, Yimin D. ; Amin, Moeness G. ; Himed, Braham
Author_Institution :
Center for Adv. Commun., Villanova Univ., Villanova, PA, USA
Volume :
9
Issue :
8
fYear :
2015
Firstpage :
1484
Lastpage :
1497
Abstract :
In this paper, we develop a novel structured Bayesian compressive sensing algorithm with location dependence for high-resolution imaging in ultra-narrowband passive synthetic aperture radar (SAR) systems. The proposed technique exploits wide-angle and/or multi-angle observations for image resolution enhancement. We first introduce a forward model based on sparse synthetic apertures. The problem of sparse scatterer imaging is formulated as an optimization problem of reconstructing group sparse signals. A logistic Gaussian kernel model, which involves a logistic function and location-dependent Gaussian kernel, and takes the correlation between entire scatterers into account, is then used to encourage the underlying continuity structure of illuminated target scene in a nonparametric Bayesian learning framework. The posterior inference of the proposed method is then provided in the Markov Chain Monte Carlo (MCMC) sampling scheme. The proposed technique enables high-resolution SAR imaging in wide-angle as well as multi-angle observation systems, and the imaging performance is improved by exploiting the underlying structure of the target scene. Simulation and experiment results demonstrate the superiority of the proposed algorithm in preserving the continuous structure and suppressing isolated components over existing state-of-the-art compressive sensing methods.
Keywords :
Gaussian processes; Markov processes; Monte Carlo methods; belief networks; compressed sensing; electromagnetic wave scattering; image enhancement; image reconstruction; image resolution; optimisation; radar imaging; radionavigation; synthetic aperture radar; Bayesian compressive sensing algorithm; Bayesian learning; MCMC sampling scheme; Markov Chain Monte Carlo sampling scheme; SAR systems; high-resolution passive SAR imaging; image resolution enhancement; location-dependent Gaussian kernel; logistic Gaussian kernel model; sparse scatterer imaging; sparse signals; sparse synthetic apertures; ultranarrowband passive synthetic aperture radar systems; Bayes methods; Compressed sensing; Image reconstruction; Passive radar; Radar imaging; Synthetic aperture radar; Bayesian compressive sensing; Passive radar; multiple-angle imaging; synthetic aperture radar (SAR); wide-angle imaging;
fLanguage :
English
Journal_Title :
Selected Topics in Signal Processing, IEEE Journal of
Publisher :
ieee
ISSN :
1932-4553
Type :
jour
DOI :
10.1109/JSTSP.2015.2479190
Filename :
7270255
Link To Document :
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