Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Christoph F. Mecklenbrauker, Peter Gerstoft, Geert Leus

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
5 Downloads (Pure)

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.

Original languageEnglish
Title of host publication2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
PublisherIEEE
Pages533-537
Number of pages5
ISBN (Electronic)978-1-5386-4752-3
ISBN (Print)978-1-5386-4753-0
DOIs
Publication statusPublished - 2018
Event10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom
Duration: 8 Jul 201811 Jul 2018
Conference number: 10

Conference

Conference10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
CountryUnited Kingdom
CitySheffield
Period8/07/1811/07/18

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.

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