ISAR imaging by exploiting the continuity of target scene

Lu Wang, Lifan Zhao, Guoan Bi, Liren Zhang

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)

    Abstract

    Compressive sensing (CS) based Inverse Synthetic Aperture Radar (ISAR) imaging exploits the sparsity of the target scene to achieve high resolution and effective denoising with limited measurements. This paper extends the CS based ISAR imaging to further include the continuity structure of the target scene within a Bayesian framework. A correlated prior is imposed to statistically encourage the continuity structures in both the cross-range and range domains of the target region and the Gibbs sampling strategy is used for Bayesian inference. Because the resulted method requires to recover the whole target scene at a time with heavy computational complexity, an approximate strategy is proposed to alleviate the computational burden. Experimental results demonstrate that the proposed algorithm can achieve substantial improvements in terms of preserving the weak scatterers and removing noise over other reported CS based ISAR imaging algorithms.

    Original languageEnglish
    Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages6072-6076
    Number of pages5
    ISBN (Print)9781479928927
    DOIs
    Publication statusPublished - Jan 1 2014
    Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
    Duration: May 4 2014May 9 2014

    Publication series

    NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
    ISSN (Print)1520-6149

    Other

    Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
    Country/TerritoryItaly
    CityFlorence
    Period5/4/145/9/14

    Keywords

    • ISAR imaging
    • continuity structures
    • model-based compressive sensing

    ASJC Scopus subject areas

    • Software
    • Signal Processing
    • Electrical and Electronic Engineering

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