NDLI: Investigation on SAR ground moving target imaging under sparse Bayesian learning framework

Content Provider	IEEE Xplore Digital Library
Author	Lei Yang Lifan Zhao Xiumei Li Guoan Bi
Copyright Year	2015
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Lei Yang; Lifan Zhao; Guoan Bi) \|\| Sch. of Inf. Sci. & Eng., Hangzhou Normal Univ., Hangzhou, China (Xiumei Li)
Abstract	In this paper, we investigate ground moving target imaging (GMTIm) by synthetic aperture radar (SAR) under sparse Bayesian learning (SBL) framework. To automatically determine the parametric dictionary used in the framework, an novel time-frequency representation method, known as Lv's distribution (LVD), is adopted, which is superior to represent multiple moving targets on the Doppler centroid frequency and chirp rate (CFCR) domain. A remarkable advantage of the SBL formulation is that a full posterior distribution can be provided for the SAR moving target image, instead of a simple point estimate as in the reported conventional methods. High order statistical information can be therefore exploited, and the imaging performance in terms of accuracy can be accordingly enhanced. To achieve an efficient Bayesian inference for the SBL implementation, an emerging technique, variational Bayesian expectation maximization (VB-EM), is employed. Both additive and multiplicative perturbations are considered in the SBL formulation, which improves the applicability of the proposed algorithm in practice. Simulation with isotropic point targets is presented to validate the effectiveness and superiority of the proposed algorithm.
Starting Page	648
Ending Page	652
File Size	514563
Page Count	5
File Format	PDF
e-ISBN	9781479919482
DOI	10.1109/ChinaSIP.2015.7230484
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-07-12
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Variational Bayesian expectation maximization (VB-EM) Additives Ground moving target imaging (GMTIm) Imaging Sparse Bayesian learning (SBL) Radar imaging Synthetic aperture radar (SAR) Bayes methods Doppler effect Lv's distribution (LVD) Clutter Synthetic aperture radar
Content Type	Text
Resource Type	Article

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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