SAR ground moving target imaging algorithm based on parametric and dynamic sparse Bayesian learning

Lei Yang, Lifan Zhao, Guoan Bi, Liren Zhang

    Research output: Contribution to journalArticlepeer-review

    62 Citations (Scopus)

    Abstract

    In this paper, a novel synthetic aperture radar (SAR) ground moving target imaging (GMTIm) algorithm is presented within a parametric and dynamic sparse Bayesian learning (SBL) framework. A new time-frequency representation, which is known as Lv's distribution (LVD), is employed on the moving targets to determine the parametric dictionary used in the SBL framework. To combat the inherent accuracy limitations of the LVD and extrinsic perturbation errors, a dynamical refinement process is further developed and incorporated into the SBL framework to obtain highly focused SAR image of multiple moving targets. An emerging inference technique, which is known as variational Bayesian expectation-maximization, is applied to achieve an efficient Bayesian inference for the focused SAR moving target image. A remarkable advantage of the proposed algorithm is to provide a fully posterior distribution (Bayesian inference) for the SAR moving target image, rather than a poor point estimate used in conventional methods. Because of utilizing high-order statistical information, the error propagation problem is desirably ameliorated in an iterative manner. The perturbations, known as the multiplicative phase error and additive clutter and noise, are both well adjusted for further improving the image quality. Experimental results by using simulated spotlight-SAR data and real Gotcha data have demonstrated the superiority of the proposed algorithm over other reported ones.

    Original languageEnglish
    Article number7422105
    Pages (from-to)2254-2267
    Number of pages14
    JournalIEEE Transactions on Geoscience and Remote Sensing
    Volume54
    Issue number4
    DOIs
    Publication statusPublished - Apr 1 2016

    Keywords

    • Ground moving target imaging (GMTIm)
    • Lv's distribution (LVD)
    • parametric and dynamic sparse Bayesian learning (Para-Dyna-SBL)
    • synthetic aperture radar (SAR)

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering
    • Earth and Planetary Sciences(all)

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