Print Email Facebook Twitter Sparse Bayesian Learning for DOA Estimation of Correlated Sources Title Sparse Bayesian Learning for DOA Estimation of Correlated Sources Author Mecklenbrauker, Christoph F. (Technische Universität Wien) Gerstoft, Peter (University of California) Leus, G.J.T. (TU Delft Signal Processing Systems) Date 2018 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. To reference this document use: http://resolver.tudelft.nl/uuid:6d53129b-ff3b-490b-bcbf-0c995e7651c2 DOI https://doi.org/10.1109/SAM.2018.8448880 Publisher IEEE Embargo date 2019-03-02 ISBN 978-1-5386-4753-0 Source 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 Event 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018, 2018-07-08 → 2018-07-11, Sheffield, United Kingdom Bibliographical note 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. Part of collection Institutional Repository Document type conference paper Rights © 2018 Christoph F. Mecklenbrauker, Peter Gerstoft, G.J.T. Leus Files PDF Sparse_Bayesian_Learning_ ... ources.pdf 319.43 KB Close viewer /islandora/object/uuid:6d53129b-ff3b-490b-bcbf-0c995e7651c2/datastream/OBJ/view