This work presents a cognitive waveform selection mechanism for chaotic ultra-wideband multiple-input multiple-output (MIMO) radars. It utilizes the target discrimination capability of a Dirichlet process mixture model (DPMM)-based clustering approach to discriminate individual extended targets and applies a mutual information (MI)-based mechanism to select the best transmission waveform. This joint DPMM-MI cognitive mechanism aims at enhancing target discrimination and detection, showing a 3-dB performance gain in achieving 0.9 target detection probability over conventional MIMO radar waveforms.


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    Titel :

    Cognitive chaotic UWB-MIMO radar based on nonparametric Bayesian technique


    Beteiligte:
    Nijsure, Yogesh (Autor:in) / Kaddoum, Georges (Autor:in) / Leung, Henry (Autor:in)


    Erscheinungsdatum :

    01.07.2015


    Format / Umfang :

    3883129 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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