Innovations based detection algorithm for correlated non-Gaussian random processes

Author

Rangaswamy, Muralidhar ; Weiner, Donald D. ; Michels, James H.

Author_Institution

Dept. of Electr. & Comput. Eng., Syracuse Univ., NY, USA

fYear

1992

fDate

7-9 Oct 1992

Firstpage

102

Lastpage

105

Abstract

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

Keywords

correlation theory; random noise; signal detection; additive correlated nonGaussian noise; algorithm; correlated nonGaussian random variables; innovations based detectors; optimal estimators; probability density function; signal detection; spherically invariant random processes; Additive noise; Covariance matrix; Detection algorithms; Detectors; Laboratories; Mean square error methods; Random processes; Sampling methods; Signal processing; Technological innovation;

fLanguage

English

Publisher

ieee

Conference_Titel

Statistical Signal and Array Processing, 1992. Conference Proceedings., IEEE Sixth SP Workshop on

Conference_Location

Victoria, BC

Print_ISBN

0-7803-0508-6

Type

conf

DOI

10.1109/SSAP.1992.246858

Filename

246858