Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Mecklenbrauker, Christoph F. (Technische Universität Wien)
Gerstoft, Peter (University of California)
Leus, G.J.T. (TU Delft Signal Processing Systems)

2018

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.

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

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

IEEE

2019-03-02

978-1-5386-4753-0

2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018

10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018, 2018-07-08 → 2018-07-11, Sheffield, United Kingdom

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2018 Christoph F. Mecklenbrauker, Peter Gerstoft, G.J.T. Leus