A sequential Bayesian beamformer FOR Gauss-Markov signals

Chun Wei J. Lam, Andrew Carl Singer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A Bayesian approach to beamforming is used to derive a sequential adaptive beamformer for estimating Gauss-Markov signals when the source direction-of-arrival (DOA) is uncertain. The DOA is assumed to be randomly selected from a discrete set of candidate directions, with a known probability mass function (PMF). Through a development similar to that of Bell, et al.[l], for i.i.d. sources, the resulting estimator becomes a weighted-combination of Kalman estimators for the source, where the observations for each estimator are retrieved using an MVDR beamformer for each of the candidate DOA's and where the relative weighting is proportional to the likelihood of the DOA given the observed data so far. Aspects of the proposed beamformer, such as robustness to DOA and asymptotic estimation performance are compared with conventional MVDR-based approaches.

Original languageEnglish (US)
Title of host publication2002 IEEE Sensor Array and Multichannel Signal Processing Workshop Proceedings, SAME 2002
PublisherIEEE Computer Society
Pages28-32
Number of pages5
ISBN (Electronic)0780375513
DOIs
StatePublished - Jan 1 2002
EventIEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2002 - Rosslyn, United States
Duration: Aug 4 2002Aug 6 2002

Publication series

NameProceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop
Volume2002-January
ISSN (Electronic)2151-870X

Other

OtherIEEE Sensor Array and Multichannel Signal Processing Workshop, SAME 2002
Country/TerritoryUnited States
CityRosslyn
Period8/4/028/6/02

ASJC Scopus subject areas

  • Signal Processing
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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