Multiobjective bilevel evolutionary approach for off-grid direction-of-arrival estimation

Yan, B; Zhao, Q; Zhang, J; Zhang, JA; Yao, X

Multiobjective bilevel evolutionary approach for off-grid direction-of-arrival estimation

Yan, B Zhao, Q Zhang, J

Zhang, JA Yao, X

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Applied Soft Computing, 2021, 113
Issue Date:: 2021-12-01

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Field	Value	Language
dc.contributor.author	Yan, B
dc.contributor.author	Zhao, Q
dc.contributor.author	Zhang, J https://orcid.org/0000-0002-6102-3762
dc.contributor.author	Zhang, JA
dc.contributor.author	Yao, X
dc.date.accessioned	2022-01-28T02:55:18Z
dc.date.available	2022-01-28T02:55:18Z
dc.date.issued	2021-12-01
dc.identifier.citation	Applied Soft Computing, 2021, 113
dc.identifier.issn	1568-4946
dc.identifier.issn	1872-9681
dc.identifier.uri	http://hdl.handle.net/10453/153732
dc.description.abstract	The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to modeling errors caused by relaxing sparse penalties, especially in impulsive noise. This paper proposes a novel idea of simultaneous source number identification and DOA estimation to address this issue. We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation. In particular, the source number is correctly exploited by the l0 norm of impinging signals without relaxations, guaranteeing accuracy. We further design a multiobjective bilevel evolutionary algorithm to solve this model. The source number identification and sparse recovery are simultaneously optimized at the on-grid (lower) level. A forward search strategy is developed to further refine the grid at the off-grid (upper) level. This strategy does not need linear approximations and can eliminate the off-grid gap with low computational complexity. Simulation results demonstrate the outperformance of our method in terms of source number and root mean square error.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Applied Soft Computing
dc.relation.isbasedon	10.1016/j.asoc.2021.107954
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0806 Information Systems
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Multiobjective bilevel evolutionary approach for off-grid direction-of-arrival estimation
dc.type	Journal Article
utslib.citation.volume	113
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0806 Information Systems
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-12-01T00:00:00+1000Z
dc.date.updated	2022-01-28T02:55:17Z
pubs.publication-status	Published
pubs.volume	113

Abstract:

The source number identification is an essential step in direction-of-arrival (DOA) estimation. Existing methods may provide a wrong source number due to modeling errors caused by relaxing sparse penalties, especially in impulsive noise. This paper proposes a novel idea of simultaneous source number identification and DOA estimation to address this issue. We formulate a multiobjective off-grid DOA estimation model to realize this idea, by which the source number can be automatically identified together with DOA estimation. In particular, the source number is correctly exploited by the l0 norm of impinging signals without relaxations, guaranteeing accuracy. We further design a multiobjective bilevel evolutionary algorithm to solve this model. The source number identification and sparse recovery are simultaneously optimized at the on-grid (lower) level. A forward search strategy is developed to further refine the grid at the off-grid (upper) level. This strategy does not need linear approximations and can eliminate the off-grid gap with low computational complexity. Simulation results demonstrate the outperformance of our method in terms of source number and root mean square error.

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http://hdl.handle.net/10453/153732