DocumentCode
526402
Title
Notice of Retraction
Multi-sensor weighted fusion algorithm based on fuzzy supported degree
Author
Li Xiao-wei ; Hu Zhen-tao ; Pan Quan ; Zhang Hong-cai
Author_Institution
Inst. of Control & Inf., Northwestern Polytech. Univ., Xi´an, China
Volume
6
fYear
2010
fDate
9-11 July 2010
Firstpage
507
Lastpage
510
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Aiming at the effective fusion of multi-sensor measurement data unknown statistics, a novel method of multi-sensor data fusion based on fuzzy supported degree is presented. The support degree distance and support degree matrix which meet membership function characteristic of fuzzy set theory, were constructed to extract and use the redundancy and complementary information, so the weight value of measurement was reasonably measure. At the same time, the method avoids the design of relation threshold and reduces the influence of prior information, and avoids the absoluteness of correlated support degree among measurement too. Finally, the simulation verifies the validity of the method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Aiming at the effective fusion of multi-sensor measurement data unknown statistics, a novel method of multi-sensor data fusion based on fuzzy supported degree is presented. The support degree distance and support degree matrix which meet membership function characteristic of fuzzy set theory, were constructed to extract and use the redundancy and complementary information, so the weight value of measurement was reasonably measure. At the same time, the method avoids the design of relation threshold and reduces the influence of prior information, and avoids the absoluteness of correlated support degree among measurement too. Finally, the simulation verifies the validity of the method.
Keywords
fuzzy set theory; redundancy; sensor fusion; statistical analysis; complementary information; fuzzy set theory; fuzzy supported degree; membership function; multisensor weighted fusion algorithm; redundancy information; statistics; support degree matrix; Robustness; data fusion; membership function; relative distance; support degree function;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-5537-9
Type
conf
DOI
10.1109/ICCSIT.2010.5563945
Filename
5563945
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