Multichannel detection for correlated non-Gaussian random processes based on innovations

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

doi:10.1117/12.154999

3 September 1993 Multichannel detection for correlated non-Gaussian random processes based on innovations

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

Proceedings Volume 1955, Signal Processing, Sensor Fusion, and Target Recognition II; (1993) https://doi.org/10.1117/12.154999
Event: Optical Engineering and Photonics in Aerospace Sensing, 1993, Orlando, FL, United States

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.

Citation Download Citation

Muralidhar Rangaswamy, Donald D. Weiner, and James H. Michels "Multichannel detection for correlated non-Gaussian random processes based on innovations", Proc. SPIE 1955, Signal Processing, Sensor Fusion, and Target Recognition II, (3 September 1993); https://doi.org/10.1117/12.154999

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