In sparse sensing based distributed multiple-input multiple-output radars, the problem of target estimation is formulated as a sparse vector recovery problem, where the vector to be recovered is block sparse, or equivalently, the sensing matrix is block diagonal and the sparse vector consists of equal-length blocks that have the same sparsity profile. This paper derives the theoretical requirements and performance guarantees for the application of sparse recovery techniques to this problem. The obtained theoretical results confirm previous, simulations-based observations that exploiting the block sparsity of the target vector can further reduce the amount of measurements needed for successful target estimation. For signal recovery, two low-complexity approaches are proposed. The first one is an alternating direction method of multipliers-based sparse signal recovery algorithm, which in addition to significantly reducing computations is also amenable to a parallel and semi-distributed implementation. The second approach decouples the location and speed estimation into two separate stages, with each stage addressing a sparse recovery problem of lower dimension while maintaining high estimation accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Distributed MIMO radar based on sparse sensing: Analysis and efficient implementation


    Contributors:


    Publication date :

    2015-10-01


    Size :

    885020 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Enhanced Automotive Sensing Assisted by Joint Communication and Cognitive Sparse MIMO Radar

    Wang, Xiangrong / Zhai, Weitong / Zhang, Xuan et al. | IEEE | 2023


    CSSF MIMO RADAR: Compressive-Sensing and Step-Frequency Based MIMO Radar

    Yao Yu / Petropulu, A. P. / Poor, H. V. | IEEE | 2012


    Sparse Representation Framework for MIMO Radar Transmit Beampattern Matching Design

    Zeng-Fei Cheng / Yong-Bo Zhao / Hui Li et al. | IEEE | 2017