Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM

Zai Yang; Xinyao Chen

doi:10.1109/Radar53847.2021.10028640

Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM

Zai Yang
, Xinyao Chen

School of Mathematics and Statistics

Xi'an Jiaotong University

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

5 Scopus citations

Abstract

The maximum likelihood estimation (MLE) is known to provide benchmark performance for direction-of-arrival (DOA) estimation. Due to high nonconvexity of the MLE problem, however, effective implementations of the MLE are rare in practice. In this paper, we consider DOA estimation with a uniform linear array and formulate by using re-parameterization and majorization minimization the stochastic MLE as a series of rank-constrained semidefinite programs that are solved using the alternating direction method of multipliers (ADMM). Numerical results are provided to illustrate the superior statistical performance of the proposed method as compared to existing approaches in the absence/presence of coherent sources.

Original language	English
Title of host publication	2021 CIE International Conference on Radar, Radar 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2376-2380
Number of pages	5
ISBN (Electronic)	9781665498142
DOIs	https://doi.org/10.1109/Radar53847.2021.10028640
State	Published - 2021
Event	2021 CIE International Conference on Radar, Radar 2021 - Haikou, Hainan, China Duration: 15 Dec 2021 → 19 Dec 2021

Publication series

Name	Proceedings of the IEEE Radar Conference
Volume	2021-December
ISSN (Print)	1097-5764
ISSN (Electronic)	2375-5318

Conference

Conference	2021 CIE International Conference on Radar, Radar 2021
Country/Territory	China
City	Haikou, Hainan
Period	15/12/21 → 19/12/21

Keywords

DOA estimation
Stochastic maximum likelihood
low rank Toeplitz matrix
majorization minimization
rank-constrained ADMM

Access to Document

10.1109/Radar53847.2021.10028640

Cite this

@inproceedings{e99f5727936f4e0793ee8e2190d6cdc3,

title = "Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM",

abstract = "The maximum likelihood estimation (MLE) is known to provide benchmark performance for direction-of-arrival (DOA) estimation. Due to high nonconvexity of the MLE problem, however, effective implementations of the MLE are rare in practice. In this paper, we consider DOA estimation with a uniform linear array and formulate by using re-parameterization and majorization minimization the stochastic MLE as a series of rank-constrained semidefinite programs that are solved using the alternating direction method of multipliers (ADMM). Numerical results are provided to illustrate the superior statistical performance of the proposed method as compared to existing approaches in the absence/presence of coherent sources.",

keywords = "DOA estimation, Stochastic maximum likelihood, low rank Toeplitz matrix, majorization minimization, rank-constrained ADMM",

author = "Zai Yang and Xinyao Chen",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 CIE International Conference on Radar, Radar 2021 ; Conference date: 15-12-2021 Through 19-12-2021",

year = "2021",

doi = "10.1109/Radar53847.2021.10028640",

language = "英语",

series = "Proceedings of the IEEE Radar Conference",

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

pages = "2376--2380",

booktitle = "2021 CIE International Conference on Radar, Radar 2021",

}

Yang, Z & Chen, X 2021, Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM. in 2021 CIE International Conference on Radar, Radar 2021. Proceedings of the IEEE Radar Conference, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 2376-2380, 2021 CIE International Conference on Radar, Radar 2021, Haikou, Hainan, China, 15/12/21. https://doi.org/10.1109/Radar53847.2021.10028640

Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM. / Yang, Zai; Chen, Xinyao.
2021 CIE International Conference on Radar, Radar 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2376-2380 (Proceedings of the IEEE Radar Conference; Vol. 2021-December).

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

TY - GEN

T1 - Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM

AU - Yang, Zai

AU - Chen, Xinyao

PY - 2021

Y1 - 2021

N2 - The maximum likelihood estimation (MLE) is known to provide benchmark performance for direction-of-arrival (DOA) estimation. Due to high nonconvexity of the MLE problem, however, effective implementations of the MLE are rare in practice. In this paper, we consider DOA estimation with a uniform linear array and formulate by using re-parameterization and majorization minimization the stochastic MLE as a series of rank-constrained semidefinite programs that are solved using the alternating direction method of multipliers (ADMM). Numerical results are provided to illustrate the superior statistical performance of the proposed method as compared to existing approaches in the absence/presence of coherent sources.

AB - The maximum likelihood estimation (MLE) is known to provide benchmark performance for direction-of-arrival (DOA) estimation. Due to high nonconvexity of the MLE problem, however, effective implementations of the MLE are rare in practice. In this paper, we consider DOA estimation with a uniform linear array and formulate by using re-parameterization and majorization minimization the stochastic MLE as a series of rank-constrained semidefinite programs that are solved using the alternating direction method of multipliers (ADMM). Numerical results are provided to illustrate the superior statistical performance of the proposed method as compared to existing approaches in the absence/presence of coherent sources.

KW - DOA estimation

KW - Stochastic maximum likelihood

KW - low rank Toeplitz matrix

KW - majorization minimization

KW - rank-constrained ADMM

UR - https://www.scopus.com/pages/publications/85174869684

U2 - 10.1109/Radar53847.2021.10028640

DO - 10.1109/Radar53847.2021.10028640

M3 - 会议稿件

AN - SCOPUS:85174869684

T3 - Proceedings of the IEEE Radar Conference

SP - 2376

EP - 2380

BT - 2021 CIE International Conference on Radar, Radar 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 CIE International Conference on Radar, Radar 2021

Y2 - 15 December 2021 through 19 December 2021

ER -

Maximum Likelihood Direction-of-Arrival Estimation via Rank-Constrained ADMM

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