We address the problem of adaptive radar detection of point-like targets in presence of Gaussian noise with unknown spectral properties, adopting a “second-order approach” to target modeling. In fact, the presence of a useful signal is modeled in terms of a rank-1 modification of the noise covariance matrix, similarly for the possible presence of a fictitious signal under the null hypothesis, aimed at increasing the selectivity of the detector. A set of homogeneous training data is assumed to be available. Results show that the proposed detectors can outperform natural competitors, especially assuming a limited number of training data.


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    Titel :

    Adaptive Radar Detectors for Point-Like Gaussian Targets in Gaussian Noise


    Beteiligte:


    Erscheinungsdatum :

    2017-06-01


    Format / Umfang :

    824649 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch







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    Tang, J. / Zhu, Z. / IEEE | British Library Conference Proceedings | 1997