NON-GAUSSIAN RANDOM VECTOR IDENTIFICATION USING SPHERICALLY INVARIANT RANDOM-PROCESSES

With the modeling of non-Gaussian radar clutter in mind, this paper presents elegant and tractable techniques for characterizing the probability density function (pdf) of a correlated non-Gaussian radar vector. The need for a library of multivariate correlated non-Gaussian pdfs in order to characterize various clutter scenarios is discussed. Specifically, the theory of spherically invariant random processes (SIRP) is examined in detail. Approaches based on the marginal envelope pdf and the marginal characteristic function have been used to obtain several multivariate non-Gaussian pdfs. Finally, an important result providing the pdf of the quadratic form or a spherically invariant random vector (SIRV) is presented. This result enables us to address the problem of distribution identification of a SIRV.