In this paper, we address the problem of radar range-Doppler imaging in the presence of clutter. Specifically, we formulate the range-Doppler imaging problem as that of recovery of a sparse vector contaminated by clutter in addition to noise. We propose a sparse Bayesian learning (SBL)-based algorithm to jointly obtain the range-Doppler image, variance of the noise, and covariance matrix of the clutter. Furthermore, we adapt a simple pruning mechanism that reduces the computational cost and improves the convergence speed.


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    Titel :

    Compressive Sensing-Based Joint Range-Doppler and Clutter Estimation


    Beteiligte:
    Salari, Soheil (Autor:in) / Chan, Francois (Autor:in) / Chan, Yiu-Tong (Autor:in) / Guay, Rudy (Autor:in)


    Erscheinungsdatum :

    01.12.2019


    Format / Umfang :

    1296819 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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