Performance analysis of the bayesian beamformer

Chunwei Jethro Lam, Andrew C. Singer

Research output: Contribution to journalConference articlepeer-review


We present an analysis of the performance of Bayesian beamformers that are able to estimate signals from unknowns source directions by balancing multiple optimal estimates according to the a posteriori probability mass function (PMF). We show that the conditional mean square error (MSE) of the Bayesian beamformer asymptotically achieves the conditional MSE of an estimator that has prior knowledge of the true direction of arrival. The convergence rate depends on both the signal-to-noise ratio (SNR) and the Kullback Leibler distance between certain probability distributions on which the Bayesian model is defined.

Original languageEnglish (US)
Pages (from-to)II197-II200
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
StatePublished - 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: May 17 2004May 21 2004

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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