Virtual-Subarray-Based Angle-of-Arrival Estimation in Analog Antenna Arrays

Qin, C; Andrew Zhang, J; Huang, X; Jay Guo, Y

Virtual-Subarray-Based Angle-of-Arrival Estimation in Analog Antenna Arrays

Qin, C

Andrew Zhang, J Huang, X

Jay Guo, Y

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Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Letters, 2020, 9 (2), pp. 194 - 197
Issue Date:: 2020-02-01

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Field	Value	Language
dc.contributor.author	Qin, C https://orcid.org/0000-0001-8869-7209	en_US
dc.contributor.author	Andrew Zhang, J	en_US
dc.contributor.author	Huang, X https://orcid.org/0000-0001-6869-5732	en_US
dc.contributor.author	Jay Guo, Y	en_US
dc.date.issued	2020-02-01	en_US
dc.identifier.citation	IEEE Wireless Communications Letters, 2020, 9 (2), pp. 194 - 197	en_US
dc.identifier.issn	2162-2337	en_US
dc.identifier.uri	http://hdl.handle.net/10453/137473
dc.description.abstract	© 2012 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms are based on beam scanning which can be time-consuming. In this letter, we propose a virtual-subarray based recursive AoA estimation scheme that can get an AoA estimate from every two measurements and recursively improve the performance by updating beamforming weights with soft probability-based information. Simulation results validate the high efficiency of the proposed scheme, demonstrating its superiority for initial AoA estimation in analog arrays.	en_US
dc.relation.ispartof	IEEE Wireless Communications Letters	en_US
dc.relation.isbasedon	10.1109/LWC.2019.2947904	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Virtual-Subarray-Based Angle-of-Arrival Estimation in Analog Antenna Arrays	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	9	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2021-10-17T00:00:00+1100
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2012 IEEE. Angle-of-arrival (AoA) estimation is a challenging problem for analog antenna arrays. Typical algorithms are based on beam scanning which can be time-consuming. In this letter, we propose a virtual-subarray based recursive AoA estimation scheme that can get an AoA estimate from every two measurements and recursively improve the performance by updating beamforming weights with soft probability-based information. Simulation results validate the high efficiency of the proposed scheme, demonstrating its superiority for initial AoA estimation in analog arrays.

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http://hdl.handle.net/10453/137473