Improved planar near-field measurement based on data assimilation

Huang, S; Zheng, J; Qin, P; Zhan, Q; Chen, X

Improved planar near-field measurement based on data assimilation

Huang, S Zheng, J Qin, P

Zhan, Q Chen, X

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Measurement: Journal of the International Measurement Confederation, 2024, 227, pp. 114265
Issue Date:: 2024-03-15

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Field	Value	Language
dc.contributor.author	Huang, S
dc.contributor.author	Zheng, J
dc.contributor.author	Qin, P https://orcid.org/0000-0001-8861-4143
dc.contributor.author	Zhan, Q
dc.contributor.author	Chen, X
dc.date.accessioned	2024-06-04T07:11:27Z
dc.date.available	2024-06-04T07:11:27Z
dc.date.issued	2024-03-15
dc.identifier.citation	Measurement: Journal of the International Measurement Confederation, 2024, 227, pp. 114265
dc.identifier.issn	0263-2241
dc.identifier.uri	http://hdl.handle.net/10453/179388
dc.description.abstract	The planar near-field measurement is widely used for antenna radiation testing because of its compact test space. However, due to the inevitable truncation error in the practical test, the measured radiation pattern of the antenna under test (AUT) is accurate only within certain confidence (angular) interval. In order to improve the measurement accuracy outside the confidence interval, this paper proposes an improved method of planar near-field measurement based on data assimilation. A reasonable mathematical model for the AUT radiation pattern is established so that the simulation data and the practical test data are fused with improved accuracy. The effectiveness of the proposed method for parameter selection has been verified experimentally, compared with the conventional method. In addition, the proposed method indicates a universal approach of complementing the measurement data with simulations or other data sources for improved accuracy.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Measurement: Journal of the International Measurement Confederation
dc.relation.isbasedon	10.1016/j.measurement.2024.114265
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0102 Applied Mathematics, 0801 Artificial Intelligence and Image Processing, 0913 Mechanical Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.title	Improved planar near-field measurement based on data assimilation
dc.type	Journal Article
utslib.citation.volume	227
utslib.for	0102 Applied Mathematics
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0913 Mechanical Engineering
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	embargoed	*
utslib.copyright.embargo	2026-03-01T00:00:00+1000Z
dc.date.updated	2024-06-04T07:11:26Z
pubs.publication-status	Published
pubs.volume	227

Abstract:

The planar near-field measurement is widely used for antenna radiation testing because of its compact test space. However, due to the inevitable truncation error in the practical test, the measured radiation pattern of the antenna under test (AUT) is accurate only within certain confidence (angular) interval. In order to improve the measurement accuracy outside the confidence interval, this paper proposes an improved method of planar near-field measurement based on data assimilation. A reasonable mathematical model for the AUT radiation pattern is established so that the simulation data and the practical test data are fused with improved accuracy. The effectiveness of the proposed method for parameter selection has been verified experimentally, compared with the conventional method. In addition, the proposed method indicates a universal approach of complementing the measurement data with simulations or other data sources for improved accuracy.

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http://hdl.handle.net/10453/179388