In a multiple-input multiple-output (MIMO) radar, adaptive multitarget tracking can be achieved using subspace tracking algorithms in conjunction with super-resolution subspace localization algorithms. However, the presence of unknown radar clutter deteriorates or breaks algorithms that rely on the white noise assumption. In this article, an $H^{\infty }$ approach is proposed for robust tracking of each target's range, direction-of-arrival and velocity with unknown clutter. Specifically, two “manifold extenders” are first proposed by combining the slow-time and fast-time dimensions of a pulse MIMO radar's received signal. Then, in the “extended” space, an $H^{\infty }$ adaptive algorithm is proposed to track an equivalent noise subspace, which exists regardless of the noise assumption. Finally, the target parameters are extracted from the adaptively tracked noise subspace. Based on computer simulation studies, the performance of the proposed $H^{\infty }$ tracking approach is evaluated using challenging tracking scenarios and compared against several existing subspace tracking algorithms that have been modified to operate on the “extended” space.


    Access

    Download


    Export, share and cite



    Title :

    MIMO Radar: An H-Infinity Approach for Robust Multitarget Tracking in Unknown Cluttered Environment


    Contributors:


    Publication date :

    2024-04-01


    Size :

    2216402 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Tracking multitarget in cluttered environment

    Sun, H.M. / Chiang, S.M. | IEEE | 1992


    Robust Multisensor Multitarget Tracker with Application to Passive Multistatic Radar Tracking

    Battistelli, G. / Chisci, L. / Morrocchi, S. et al. | IEEE | 2012



    Multitarget tracking procedures for automotive radar networks

    Oprisan, Dan / Rohling, Hermann | Tema Archive | 2003


    A Reinforcement Learning Based Approach for Multitarget Detection in Massive MIMO Radar

    Ahmed, Aya Mostafa / Ahmad, Alaa Alameer / Fortunati, Stefano et al. | IEEE | 2021