Since the dwell time of a frequency diverse array multiple-input–multiple-output radar is very short and allows to generate a smaller set of useful transmitted samples than the data dimension under test (i.e., the positive covariance matrix cannot be obtained), the overall detection performance deteriorates drastically. To address this challenge, we propose a reduced-dimensional (RD) approach in this article. Specifically, based on the RD transformed data model, we introduce a detector utilizing the generalized likelihood ratio test (GLRT) criterion, named RD-GLRT. Furthermore, assuming that the transmit–receive vector (or Doppler vector) belongs to a given transmit–receive (or Doppler) subspace, we premultiply the test data with an appropriate matrix representing the transmit–receive subspace, resulting in a reduction in test data volume. Subsequently, we introduce two additional adaptive detectors, namely, the RD subspace-based GLRT and the Wald criteria detectors, aiming for enhanced detection performance. Notably, all three proposed detectors exhibit a constant false alarm rate property against the covariance matrix and demonstrate a reduced demand for test data volume. Numerical results validate the effectiveness of these proposed detectors.


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    Titel :

    FDA-MIMO Radar Target Detection With Limited Test Samples


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


    Erscheinungsdatum :

    01.08.2024


    Format / Umfang :

    1656020 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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