We design three statistical tests to ascertain whether radar data comply with the hypotheses of multivariate Gaussianity, spatial homogeneity, and covariance persymmetry, respectively. For the first issue we develop a statistical procedure based on quadratic distributional distances, which exploits the representation of Gaussian vectors in generalized spherical coordinates. As to the spatial homogeneity we propose a technique, based on the Kolmogorov-Smirnov (KS) test, relying on the properties of quadratic forms constructed from Gaussian vectors and Wishart distributed matrices. Finally, in order to analyze the persymmetry property of the disturbance covariance matrix, we design a testing procedure based on the generalized likelihood ratio test (GLRT). We thus apply the new tests to L-band experimentally measured clutter data, collected by the MIT Lincoln Laboratory Phase One radar, at the Katahdin Hill site. The results show that the multivariate Gaussian hypothesis for the considered data file is reasonable. On the contrary the assumption of spatial homogeneity can be done only within small clutter regions which, in general, exhibit also a persymmetric covariance matrix.


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    Title :

    Statistical tests for higher order analysis of radar clutter: their application to L-band measured data


    Contributors:
    Conte, E. (author) / De Maio, A. (author) / Farina, A. (author)


    Publication date :

    2005-01-01


    Size :

    1184238 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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