Optimal detection of non-Gaussian random signals in Gaussian noise

Doron Kletter, Peter M. Schultheiss, Hagit Messer

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A method of detecting arbitrary random signals in the presence of additive Gaussian noise is discussed. The method is based on eigendecomposition of the noise probability subspace. This decomposition leads to unique detector structure, invariant with respect to the signal distribution. The resulting detection scheme uses a variable number of terms per decision, but any decision made is exactly the same as the one produced by the optimal detector. The scheme is found to be computationally efficient, and well suited for generalization to the case of unknown (or only partially known) statistics. The efficiency of the algorithm is demonstrated in a typical example, with the aid of computer simulations.

Original languageEnglish
Title of host publicationProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Editors Anon
PublisherPubl by IEEE
Pages1305-1308
Number of pages4
ISBN (Print)078030033
StatePublished - 1991
EventProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91 - Toronto, Ont, Can
Duration: 14 May 199117 May 1991

Publication series

NameProceedings - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing
Volume2
ISSN (Print)0736-7791

Conference

ConferenceProceedings of the 1991 International Conference on Acoustics, Speech, and Signal Processing - ICASSP 91
CityToronto, Ont, Can
Period14/05/9117/05/91

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