Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Conference paper (2018)

Authors

Christoph F. Mecklenbrauker Technische Universität Wien

Peter Gerstoft University of California

Geert Leus Signal Processing Systems -

G.J.T. Leus Signal Processing Systems -

G. Leus Signal Processing Systems -

Research Group

Signal Processing Systems () (TU Delft)

DOI: https://doi.org/10.1109/SAM.2018.8448880

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http://resolver.tudelft.nl/uuid:6d53129b-ff3b-490b-bcbf-0c995e7651c2

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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

Direction of arrival (DOA) estimation from array observations in a noisy environment is discussed. The source amplitudes are assumed to be correlated zero-mean complex Gaussian distributed with unknown covariance matrix. The DOAs and covariance parameters of plane waves are estimated from multi-snapshot sensor array data using sparse Bayesian learning (SBL). The performance of SBL is evaluated in terms of the fidelity of the reconstructed coherency matrix of the estimated plane waves.

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