Weighted conflict evidence fusion method based on Hellinger distance and reliability entropy

Abstract

The invention discloses a weighted conflict evidence fusion method based on a Hellinger distance and reliability entropy. The method comprises the following steps: firstly, acquiring measurement information of a plurality of sensors, converting the measurement information into evidence information, then converting focal elements in fused evidences into single subset focal elements by utilizing a basic probability assignment conversion formula, and introducing a Hellinger distance to obtain the support degree of the fused evidences; besides, determining the trust degree of the fused evidence byrepresenting the uncertainty degree of the evidence by improving the reliability entropy and comprehensively considering the Hellinger distance and the improved reliability entropy, obtaining a weight factor, then correcting the fused evidence by utilizing a weighted average thought, finally fusing the corrected evidences one by one by adopting a Dempster combination rule, and outputting a finaltarget identification decision result. Compared with a traditional algorithm, through the basic probability assignment conversion function and the Hellinger distance, the conflict degree between the proposition evidences of the non-single subset can be effectively measured; meanwhile, the uncertainty degree of the evidence is represented through the improved reliability entropy, the weight coefficient of the fused evidence is jointly determined by comprehensively considering the support degree and the information amount, and the method has 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 weighted conflict evidence fusion method based on Hellinger distance and reliability entropy. Background technique [0002] Target recognition is the basis of battlefield situation assessment and threat assessment, and is also an important basis for firepower allocation and strikes on targets. However, in the complex battlefield environment, due to the harsh external environmental factors, human countermeasures and deception, and the errors of the sensors themselves, the information provided by each sensor is generally characterized by uncertainty, incompleteness, and high conflict. Identification results provided by sensors such as sensors, electronic support measures, and IFF can be highly conflicting, and relatively unreliable sources of evidence can sometimes lead to erroneous decisions. When dealing with uncertain information, it is difficult for a...

AI Technical Summary

Problems solved by technology

However, in the case of high conflicts in fusion evidence, the Dempster combination rule completely ignores contradictory information during the normalization process, which leads to unreasonable problems in mathematics, so that conclusions that are contrary to intuition are produced, and effective decision-making cannot be made, which limits the D-S evidence theory. further development and improvement of

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