Conflict evidence fusion method based on arithmetic average proximity

Abstract

The invention discloses a conflict evidence fusion method based on arithmetic average proximity. The method comprises the following steps of obtaining the measurement information of a plurality of sensors, selecting a proper method according to an actual application scene to obtain BPA of evidence, converting the BPA into evidence information, introducing an arithmetic average close degree conceptin a fuzzy theory to measure the mutual support degree of the evidence on the same focal element, and calculating a weight coefficient of the fusion evidence by utilizing the arithmetic average closedegree between the evidence; and finally, fusing the corrected evidence one piece by one piece by adopting a Dempster combination rule, and outputting a decision result of final target identification. According to the invention, the arithmetic average close degree method in fuzzy mathematics is introduced; the mutual support degree of each evidence to the same proposition is measured by using thearithmetic average close degree of the basic probability assignment of the same focal element in the evidences, and the corrected evidence is fused one piece by one piece by using the Dempster combination rule after the evidence is corrected, so that the method has the important theoretical significance and application value.

Description

technical field [0001] The invention relates to the technical field of multi-source information fusion, in particular to a conflict evidence fusion method based on arithmetic mean closeness. Background technique [0002] With the continuous emergence of multi-sensor systems facing complex application backgrounds, due to the diversity of information provided by sensor systems, large information capacity, and information processing speed, etc. have been far higher than the comprehensive information processing capabilities of the human brain. , information fusion technology was born. Information fusion technology is the use of computer technology to automatically analyze and comprehensively process the observation information of multiple sensors under certain criteria, in order to complete the required decision-making and estimation tasks. Level and other processing, to obtain more and more effective information than any single sensor, to expand the space and time coverage, to...

AI Technical Summary

Problems solved by technology

[0004] Due to the influence of sensor accuracy, external environment interference, and human factors, the identification target information provided by the sensor is incomplete, imprecise, vague, and may even contain conflicting or contradictory information, making the multi-source uncertain information Efficient integration has become an urgent problem to be solved

However, when the Dempster combination rule solves the fusion of low-confidence and high-conflict evidence, it often produces conclusions that are counterintuitive, and cannot make effective decisions, which greatly affects the decision-making performance of the fusion system, and to a certain extent restricts evidence theory in information Applications in Converged Domains

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