Doa estimation in heteroscedastic noise with sparse Bayesian learning

Conference paper (2018)

Authors

Peter Gerstoft University of California
Santosh Nannuru University of California
Christoph F. Mecklenbrauker Technische Universität Wien
G.J.T. Leus Signal Processing Systems - EEMCS
Research Group
Signal Processing Systems (EEMCS) (TU Delft)
Copyright: © 2018 Peter Gerstoft, Santosh Nannuru, Christoph F. Mecklenbrauker, G.J.T. Leus
DOI: https://doi.org/10.1109/ICASSP.2018.8462447
Sparsity Sparse Bayesian learning Heteroscedastic noise DOA estimation
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Signal Processing Systems

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.