Maximum likelihood localization of wideband sources

Miriam A. Doron, Anthony J. Weiss

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

Abstract

In this paper we examine the Gaussian and deterministic ML DOA estimates for several cases of a-priori noise statistics. We compare the structure of the Gaussian and the deterministic log-likelihood functions are show that for unknown noise spectrum, both estimators minimize the entropy of the measurements. We also shown that the Gaussian log-likelihood function includes an additional term, which is the entropy of the projection of the measurements onto the signal-subspace. We derive General conditions under which the deterministic ML estimate is also an extremum of the Gaussian likelihood function and shown that the asymptotic (large K) limit of these conditions guarantees equal asymptotic performances. We also shown that the asymptotic performance is not affected by prior knowledge of the noise spectrum.

Original languageEnglish
Title of host publicationICASSP 1992 - 1992 International Conference on Acoustics, Speech, and Signal Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages489-492
Number of pages4
ISBN (Electronic)0780305329
DOIs
StatePublished - 1992
Event1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992 - San Francisco, United States
Duration: 23 Mar 199226 Mar 1992

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2
ISSN (Print)1520-6149

Conference

Conference1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1992
Country/TerritoryUnited States
CitySan Francisco
Period23/03/9226/03/92

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