Innovations based detection algorithm for correlated non-Gaussian random processes

This paper addresses the problem of detecting a known signal in additive correlated nonGaussian noise using the innovations approach. There is no unique specification for the joint probability density function (PDF) of N correlated nonGaussian random variables. The authors overcome this problem by using the theory of spherically invariant random processes (SIRP) and derive the innovations based detectors. The optimal estimators for obtaining the innovations processes are linear and the resulting detector is canonical for the class of PDFs arising from SIRPs