Since frequency diverse array multiple-input multiple-output (FDA-MIMO) radar possesses additional target range information for potential performance improvement, this article studies adaptive distributed targets detection for FDA-MIMO radar, where the targets are embedded in Gaussian clutter with unknown covariance matrix. The proposed FDA-MIMO radar detection model considers also the distributed targets occupying several secondary range cells, which is different from the classic detection models in multiple-input multiple-output (MIMO) and/or phase array (PA) radars that discuss only point-like targets. By exploiting the FDA-MIMO radar framework for distributed target detection, we propose the detector through a two-step generalized likelihood ratio test criteria without the need of training data and/or a priori covariance matrix. Moreover, closed-form expressions for the probability of false alarm and detection probability are derived, respectively. The proposed detector adheres to the property of a constant false alarm rate because its probability of false alarm is not restricted by the covariance matrix. The proposed method together with all theoretical analysis are verified by numerical results.


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    Titel :

    Adaptive Distributed Target Detection for FDA-MIMO Radar in Gaussian Clutter Without Training Data


    Beteiligte:
    Huang, Bang (Autor:in) / Jian, Jiangwei (Autor:in) / Basit, Abdul (Autor:in) / Gui, Ronghua (Autor:in) / Wang, Wen-Qin (Autor:in)


    Erscheinungsdatum :

    2022-08-01


    Format / Umfang :

    1479397 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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