Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Christoph F. Mecklenbrauker; Peter Gerstoft; Geert Leus

doi:10.1109/SAM.2018.8448880

Sparse Bayesian Learning for DOA Estimation of Correlated Sources

Christoph F. Mecklenbrauker, Peter Gerstoft, Geert Leus

Signal Processing Systems

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

4 Citations (Scopus)

42 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 language	English
Title of host publication	2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Publisher	IEEE
Pages	533-537
Number of pages	5
ISBN (Electronic)	978-1-5386-4752-3
ISBN (Print)	978-1-5386-4753-0
DOIs	https://doi.org/10.1109/SAM.2018.8448880
Publication status	Published - 2018
Event	10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 - Sheffield, United Kingdom Duration: 8 Jul 2018 → 11 Jul 2018 Conference number: 10

Conference

Conference	10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018
Country/Territory	United Kingdom
City	Sheffield
Period	8/07/18 → 11/07/18

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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10.1109/SAM.2018.8448880

Sparse_Bayesian_Learning_for_DOA_Estimation_of_Correlated_SourcesFinal published version, 238 KB

Cite this

@inproceedings{6d53129bff3b490bbcbf0c995e7651c2,

title = "Sparse Bayesian Learning for DOA Estimation of Correlated Sources",

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.",

author = "Mecklenbrauker, {Christoph F.} and Peter Gerstoft and Geert Leus",

note = "Green Open Access added to TU Delft Institutional Repository {\textquoteleft}You share, we take care!{\textquoteright} – 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.; 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018 ; Conference date: 08-07-2018 Through 11-07-2018",

year = "2018",

doi = "10.1109/SAM.2018.8448880",

language = "English",

isbn = "978-1-5386-4753-0",

pages = "533--537",

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

publisher = "IEEE",

address = "United States",

}

Mecklenbrauker, CF, Gerstoft, P & Leus, G 2018, Sparse Bayesian Learning for DOA Estimation of Correlated Sources. in 2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018., 8448880, IEEE, pp. 533-537, 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018, Sheffield, United Kingdom, 8/07/18. https://doi.org/10.1109/SAM.2018.8448880

Sparse Bayesian Learning for DOA Estimation of Correlated Sources. / Mecklenbrauker, Christoph F.; Gerstoft, Peter; Leus, Geert.
2018 IEEE 10th Sensor Array and Multichannel Signal Processing Workshop, SAM 2018. IEEE, 2018. p. 533-537 8448880.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Gerstoft, Peter

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N2 - 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.

AB - 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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DO - 10.1109/SAM.2018.8448880

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SN - 978-1-5386-4753-0

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

PB - IEEE

T2 - 10th IEEE Sensor Array and Multichannel Signal Processing Workshop, SAM 2018

Y2 - 8 July 2018 through 11 July 2018

ER -

Sparse Bayesian Learning for DOA Estimation of Correlated Sources

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