Doa Estimation in Heteroscedastic Noise with Sparse Bayesian Learning

Peter Gerstoft, Santosh Nannuru, Christoph F. Mecklenbrauker, Geert Leus
2018 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
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
more » ... d (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.
doi:10.1109/icassp.2018.8462447 dblp:conf/icassp/GerstoftNML18 fatcat:jxw6iubgyfhnvfxdntbn4r6v44