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Proceedings Paper

Multichannel detection for correlated non-Gaussian random processes based on innovations
Author(s): Muralidhar Rangaswamy; Donald D. Weiner; James H. Michels
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Paper Abstract

This paper addresses the problem of multichannel signal detection in additive correlated non- Gaussian noise using the innovations approach. While this problem has been addressed extensively for the case of additive Gaussian noise, the corresponding problem for the non- Gaussian case has received limited attention. This is due to the fact that there is no unique specification for the joint probability density function (PDF) of N correlated non-Gaussian random variables. We overcome this problem by using the theory of spherically invariant random processes (SIRP) and derive the innovations based detectors. It is found that the optimal estimators for obtaining the innovations processes are linear and that the resulting detector is canonical for the class of PDFs arising from SIRPs.

Paper Details

Date Published: 3 September 1993
PDF: 12 pages
Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); doi: 10.1117/12.154999
Show Author Affiliations
Muralidhar Rangaswamy, Rome Lab. (United States)
Donald D. Weiner, Syracuse Univ. (United States)
James H. Michels, Rome Lab. (United States)

Published in SPIE Proceedings Vol. 1955:
Signal Processing, Sensor Fusion, and Target Recognition II
Ivan Kadar; Vibeke Libby, Editor(s)

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