Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | II197-II200 |
| Journal | ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings |
| Volume | 2 |
| State | Published - 2004 |
| Event | Proceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada Duration: May 17 2004 → May 21 2004 |
ASJC Scopus subject areas
- Software
- Signal Processing
- Electrical and Electronic Engineering