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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    Title :

    Compressive Sensing-Based Joint Range-Doppler and Clutter Estimation


    Contributors:


    Publication date :

    2019-12-01


    Size :

    1296819 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



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