This paper proposes a new radar detection architecture, which is composed of an interference covariance structure classifier and a bank of adaptive radar detectors. The classifier is based on model order selection theory and the Bayesian information criterion to determine the covariance structure that is deemed to be suitable for a specific set of radar data [1]. This decision stage drives the choice of the radar detector within a specific class of adaptive detectors to establish the possible target presence. The critical issue concerning the constant false alarm rate behavior of the architecture is discussed and two techniques for the threshold setting process are proposed. Finally, the detection performance analysis, conducted on both simulated and measured data, shows that the proposed architecture can guarantee better performance than classic radar decision schemes in scenarios where the interference covariance exhibits structural properties.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Radar Detection Architecture Based on Interference Covariance Structure Classification


    Contributors:


    Publication date :

    2019-04-01


    Size :

    1851628 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    PolSAR Covariance Structure Detection and Classification based on the EM Algorithm

    Han, Sudan / Addabbo, Pia / Biondi, Filippo et al. | IEEE | 2022


    Detection and diagnosis of radar modeling errors using covariance consistency

    Register, Andy H. / Mallik, Mahendra / Blair, W. Dale et al. | IEEE | 2009


    Region Covariance: A Fast Descriptor for Detection and Classification

    Tuzel, O. / Porikli, F. / Meer, P. | British Library Conference Proceedings | 2006