LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES

Xinyao Chen; Zai Yang

doi:10.1109/ICASSP43922.2022.9747249

LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES

Xinyao Chen
, Zai Yang

School of Mathematics and Statistics

Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform linear array.

Original language	English
Title of host publication	2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5013-5017
Number of pages	5
ISBN (Electronic)	9781665405409
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9747249
State	Published - 2022
Event	2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 - Hybrid, Singapore Duration: 22 May 2022 → 27 May 2022

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2022-May
ISSN (Print)	1520-6149

Conference

Conference	2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022
Country/Territory	Singapore
City	Hybrid
Period	22/05/22 → 27/05/22

Keywords

Direction-of-arrival estimation
coherent sources
maximum likelihood estimation
more sources than sensors
rank-constrained ADMM
sparse linear array

Access to Document

10.1109/ICASSP43922.2022.9747249

Cite this

Chen, X., & Yang, Z. (2022). LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings (pp. 5013-5017). (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP43922.2022.9747249

Chen, Xinyao ; Yang, Zai. / LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES. 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 5013-5017 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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title = "LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES",

abstract = "DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform linear array.",

keywords = "Direction-of-arrival estimation, coherent sources, maximum likelihood estimation, more sources than sensors, rank-constrained ADMM, sparse linear array",

author = "Xinyao Chen and Zai Yang",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE; 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022 ; Conference date: 22-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.1109/ICASSP43922.2022.9747249",

language = "英语",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "5013--5017",

booktitle = "2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings",

}

Chen, X & Yang, Z 2022, LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES. in 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2022-May, Institute of Electrical and Electronics Engineers Inc., pp. 5013-5017, 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022, Hybrid, Singapore, 22/05/22. https://doi.org/10.1109/ICASSP43922.2022.9747249

LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES. / Chen, Xinyao; Yang, Zai.
2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2022. p. 5013-5017 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2022-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2022

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N2 - DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform linear array.

AB - DOA estimation with sparse linear arrays has been extensively studied, with an emphasis on localizing more sources than sensors. A critical assumption in previous studies however is that the sources are all uncorrelated. In this paper, we present an algorithm that is shown to be able to localize more sources than sensors in presence of correlated or coherent sources without the knowledge of the source coherence structure. Our algorithm is generalized from our recently proposed rank-constrained ADMM approach to maximum likelihood estimation for uncorrelated sources with a uniform linear array.

KW - Direction-of-arrival estimation

KW - coherent sources

KW - maximum likelihood estimation

KW - more sources than sensors

KW - rank-constrained ADMM

KW - sparse linear array

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DO - 10.1109/ICASSP43922.2022.9747249

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BT - 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2022

Y2 - 22 May 2022 through 27 May 2022

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Chen X, Yang Z. LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES. In 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2022. p. 5013-5017. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP43922.2022.9747249

LOCALIZING MORE SOURCES THAN SENSORS IN PRESENCE OF COHERENT SOURCES

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