Doa Estimation in Heteroscedastic Noise with Sparse Bayesian Learning
Peter Gerstoft, Santosh Nannuru, Christoph F. Mecklenbräuker, Geert Leus
Abstract
The paper considers direction of arrival (DOA) estimation from long-term observations in a noisy environment. In such an environment the noise source might evolve, causing the stationary models to fail. Therefore a heteroscedastic Gaussian noise model is introduced where the variance can vary across observations and sensors. The source amplitudes are assumed independent zero-mean complex Gaussian distributed with unknown variances (i.e. the source powers), leading to stochastic maximum likelihood (ML) DOA estimation. The DOAs of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL) where the noise is estimated across both sensors and snapshots. Simulations demonstrate that taking the heteroscedastic noise into account improves DOA estimation.
BibTeX
@inproceedings{icassp2018_doaestimationinh,
title = {Doa Estimation in Heteroscedastic Noise with Sparse Bayesian Learning},
author = {Peter Gerstoft and Santosh Nannuru and Christoph F. Mecklenbräuker and Geert Leus},
booktitle = {ICASSP 2018},
year = {2018}
}