Multi-sensor Information Fusion Method Based on Evidence Similarity

A fusion method and multi-sensor technology, applied in the field of multi-sensor information fusion technology and D-S evidence theory, can solve the problems of unsatisfactory and incomplete synthesis effects, avoid unreasonable results, reduce the impact of conflicting evidence, and speed up Effects of Speed ​​and Accuracy
CN105046067BActive Publication Date: 2017-12-29NORTHWESTERN POLYTECHNICAL UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
NORTHWESTERN POLYTECHNICAL UNIV
Publication Date
2017-12-29

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Abstract

The present invention provides a multi-sensor information fusion method based on evidence similarity. The method comprises: firstly, representing multi-sensor decision layer data by using evidence; secondly, performing Pignistic probability conversion on each piece of evidence, calculating the similarity between each two pieces of evidence after the conversion, and determining the supporting degree and the reliability degree of each piece of evidence; and finally, correcting an evidence source, and performing evidence synthesis. According to the multi-sensor information fusion method based on evidence similarity, reasonable measurement and correction are performed on the conflict degree by using existing evidence information, so that the influence of conflict evidence is reduced, the generation of an unreasonable result is avoided, the speed of multi-sensor information fusion is increased, and the precision of multi-sensor information fusion is improved.
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Description

technical field

[0001] The invention relates to a multi-sensor information fusion technology and D-S evidence theory. Background technique

[0002] The D-S evidence theory was proposed by Dempster in 1967, and then promoted and developed by Shafer in 1976, forming an uncertain reasoning method. D-S evidence theory, with its advantages in the comprehensive processing of uncertainty and inaccurate information, can better deal with the uncertainty problems caused by randomness and ambiguity without prior probability and conditional probability density. , and by introducing trust function and likelihood function to distinguish "don't know" from "uncertainty", it has been widely used in information fusion, pattern recognition, decision analysis and fault diagnosis and other fields in recent years.

[0003] Multi-sensor information fusion refers to the coordinated use of multiple sensors, the integration of local incomplete observations provided by multiple sensors of the same ty...

Claims

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