With the modeling of non-Gaussian radar clutter in mind, elegant and tractable techniques are presented for characterizing the probability density function (PDF) of a correlated non-Gaussian radar vector. The need for a library of multivariable correlated non-Gaussian PDFs in order to characterize various clutter scenarios is discussed. Specifically,. the theory of spherically invariant random processes (SIRPs) 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. An important result providing the PDF of the quadratic form of a spherically invariant random vector (SIRV) is presented. This result enables the problem of distributed identification of a SIRV to be addressed.<>
Non-Gaussian random vector identification using spherically invariant random processes
IEEE Transactions on Aerospace and Electronic Systems ; 29 , 1 ; 111-124
1993-01-01
1737717 byte
Article (Journal)
Electronic Resource
English
Non-Gaussian Random Vector Identification Using Spherically Invariant Random Processes
Online Contents | 1993
|Numerical simulation of homogeneous non-Gaussian random vector fields
Online Contents | 1993
|Limitations of Radiometer Performance in Spherically Invariant Noise
Online Contents | 1995
|