A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

Gao, L; Yin, M; Xiao, F; Cao, Z

A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making

Gao, L Yin, M Xiao, F Cao, Z

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Conference on Unmanned Systems (ICUS), 2023, 00, pp. 299-304
Issue Date:: 2023-11-21

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Field	Value	Language
dc.contributor.author	Gao, L
dc.contributor.author	Yin, M
dc.contributor.author	Xiao, F
dc.contributor.author	Cao, Z https://orcid.org/0000-0003-3656-0328
dc.date	2023-10-13
dc.date.accessioned	2024-04-15T00:15:53Z
dc.date.available	2024-04-15T00:15:53Z
dc.date.issued	2023-11-21
dc.identifier.citation	2023 IEEE International Conference on Unmanned Systems (ICUS), 2023, 00, pp. 299-304
dc.identifier.isbn	9798350316308
dc.identifier.uri	http://hdl.handle.net/10453/177911
dc.description.abstract	Effective target detection and recognition are essential in remote sensing image field and complex evidence theory CET is widely used for this purpose However measuring conflict between complex basic belief assignments CBBAs in CET is challenging This study proposes a complex belief Jensen Shannon divergence based on the complex Pignistic transformation to measure conflict accounting for quantum interference effects in CBBAs We analyze the properties of the CBJS divergence including boundedness nondegeneracy and symmetry Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system Moreover an algorithm for decision making is presented and applied in pattern recognition illustrating its superiority
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Conference on Unmanned Systems (ICUS)
dc.relation.ispartof	2023 IEEE International Conference on Unmanned Systems
dc.relation.isbasedon	10.1109/icus58632.2023.10318369
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	A Complex Belief Jensen-Shannon Divergence in Complex Evidence Theory for Decision-Making
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Hefei, China
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 Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-15T00:15:51Z
pubs.finish-date	2023-10-15
pubs.publication-status	Published
pubs.start-date	2023-10-13
pubs.volume	00

Abstract:

Effective target detection and recognition are essential in remote sensing image field and complex evidence theory CET is widely used for this purpose However measuring conflict between complex basic belief assignments CBBAs in CET is challenging This study proposes a complex belief Jensen Shannon divergence based on the complex Pignistic transformation to measure conflict accounting for quantum interference effects in CBBAs We analyze the properties of the CBJS divergence including boundedness nondegeneracy and symmetry Numerical examples are presented to show the effectiveness of the conflict measure method and the potential of improving the robustness of intelligent interpretation system Moreover an algorithm for decision making is presented and applied in pattern recognition illustrating its superiority

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http://hdl.handle.net/10453/177911