Conflict evidence fusion method based on arithmetic average proximity

A technique of arithmetic mean and fusion method, which is applied in the field of conflict evidence fusion based on arithmetic mean closeness.

Active Publication Date: 2019-07-19
HENAN UNIVERSITY
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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
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  • Conflict evidence fusion method based on arithmetic average proximity
  • Conflict evidence fusion method based on arithmetic average proximity
  • Conflict evidence fusion method based on arithmetic average proximity

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Embodiment Construction

[0022] Such as figure 1 Shown, the present invention comprises the following steps:

[0023] A. By obtaining the basic probability assignment of multiple sensor measurement information corresponding to the evidence focal element, each evidence is regarded as a vector, and the vector of the i-th evidence is represented by m i where i=1,2,...,n, n is the total number of evidence vectors, p is the number of focal elements in the identification frame Θ; evidence, and treat each fused evidence as a vector. Assume that n pieces of evidence are obtained, respectively m 1 ,m 2 ,...,m n , assuming that the focal element in the identification frame Θ is θ 1 ,θ 2 ,…,θ p , the basic probability assignments of focal elements corresponding to the i-th evidence are m i (θ 1 ),m i (θ 2 ),…,m i (θ p ), the evidence is regarded as a vector, then the elements corresponding to the i-th evidence vector are m i (θ 1 ),m i (θ 2 ),…,m i (θ p ).

[0024] In the process of obtaining...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N7/00
CPCG06N7/01G06F18/25
Inventor 李军伟胡振涛周林刘先省金勇
Owner HENAN UNIVERSITY
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