In this article, the problem of robust sparse beamforming (RSB) design for a receive array is presented, based on minimax and maximin signal-to-interference-plus-noise ratio (SINR) optimization criteria. The framework hybridizes weighted l1-norm (or its squared version) regularization (to manage the beamvector's sparsity) with modern robust adaptive beamforming methods, when the uncertainty sets for the parameters in the SINR formula are convex and the feasible set of the beamvectors contains at least a cardinality constraint. In addition, three main challenges of the RSB designs are discussed, including the scenarios with nonconvex uncertainty sets for SINR parameters, general-rank covariance matrix of the signal of interest (SOI), and multiple statistically independent SOI sources. Finally, five future research directions for the RSB designs are pointed out.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Emerging Trends in Radar: Robust Sparse Beamforming for Radar Array Processing


    Beteiligte:
    He, Jianfeng (Autor:in) / De Maio, Antonio (Autor:in) / Huang, Yongwei (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.06.2025


    Format / Umfang :

    635996 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Studies of Subarray Adaptive Digital Beamforming for Sparse Phased Array Radar

    Qiu, L. / Zhou, Z. / Liang, D. et al. | British Library Conference Proceedings | 1996


    Studies of subarray adaptive digital beamforming for sparse phased array radar

    Qiu Lijun / Zhou Zhimin / Liang Diannong | IEEE | 1996


    Emerging Trends in Radar: HF Skywave Radar

    Frazer, Gordon J. / Williams, Charlie G. | IEEE | 2025


    Emerging Trends in Radar: Automotive Radar Networks

    Ram, Shobha Sundar / Ghatak, Gourab | IEEE | 2025


    Emerging Trends in Radar: Natural Language Processing

    Narayanan, Ram M. / Zaunegger, Jackson S. / Singerman, Paul G. et al. | IEEE | 2025