Weighted conflict evidence fusion method based on fuzzy classification

Abstract

The invention discloses a weighted conflict evidence fusion method based on fuzzy classification, and the method comprises the following steps: firstly, obtaining the information of a plurality of evidences through a sensor, converting the evidences into single subset evidences through an interval probability assignment conversion formula, taking the evidences as vectors, obtaining the membership degree between the evidences, then, solving a fuzzy equivalence matrix through the membership matrix, and carrying out fuzzy classification on the evidence according to a threshold value; calculating the mutual support degree between the same type of evidences, obtaining the weight of each evidence in the same type of evidences based on the mutual support degree, and carrying out weighted average fusion on the evidences; and obtaining the fused new evidence, obtaining the weight of each evidence by combining the correlation support degree between the evidence and the credibility obtained by each evidence, and after the evidence is subjected to weighted fusion, carrying out secondary DS fusion. According to the method, fuzzy classification is carried out on the evidences, the weights of the evidences belonging to the same type and the evidences belonging to different types are obtained by adopting different methods, and the method has important theoretical significance and application value.

Description

Technical field [0001] The present invention relates to the field of target recognition, and more particularly to a fusion method based on fuzzy classification. Background technique [0002] At present, the target identification technology handles and fuses the information acquired by multiple sensors, and can effectively avoid the limitations of single sensor decisions, but due to external reasons or sensor itself, collected data may not be very reliable, even collect Wrong information, etc., this will lead to an error in decision. The essence of Dempster-Shafer (DS) Evidence is a promotion of probability theory, which expands probabilities of many basic events into a basic event, and establishes basic probability assignment functions in this space. Compared to traditional probability theory, DS evidence theory not only effectively expresses random uncertainty, but also better expresss incomplete information and subjective uncertain information, and compared with Bayesian theory...

AI Technical Summary

Problems solved by technology

In order to solve highly conflicting evidence, Yager made some improvements to the evidence theory. Considering that the conflict cannot be determined, the conflict is assigned to an unknown proposition. When the basic probability of an event is assigned a value of 0, the result of the final basic probability assignment will be 0, even if other evidence has a higher basic probability assignment for the event, it will not affect the composite result

In order to solve the problem of fusion of conflicting evidence, literature [1] proposes a simple average method to eliminate the impact of conflicting evidence on fusion, but this method is only a simple average of all evidence, and does not consider the mutual relationship between evidence. Influence, literature [2] proposed a classification and fusion algorithm of evidence theory based on Deng entropy, with entropy reduction as the main idea, to classify and fuse evidence

In the decision-making process, the category fusion result containing the most evidence is used as the overall fusion result to avoid the influence of high conflict evidence and improve the information validity of the fusion result. However, only the category fusion result containing the most evidence is used as the overall fusion result. , without considering the fusion results of other categories, it will cause some information to be lost, which will adversely affect the accuracy of the results

Images