A Weighted Evidence Combination Method for Multisensor Data Fusion

Abstract

Multisensor information fusion exerts a key part in lots of practical usages. Dempster-shafer evidence theory has drawn extensive attention in many scopes of information fusion due to its flexibility and effectiveness in dealing with uncertain data without aforehand data. But when combining highly contradictory evidence with Dempster's combinatorial principles, it can result in counterintuitive results. To solve the issue, the study proposes a multi-sensor data weighted evidence combination fusion method based on inter-evidence difference measure. Firstly, different measures including evidence distance and conflict are with the definition of characterizing distinctions between the two pieces of evidence. Then, according to the difference between each evidence and the average evidence, the weight coefficients of each evidence are calculated. In the end, initial evidence is discounted according to weighting factor, as well as Dempster's combination principle is adopted to discount the evidence for fusion. Many instances show that this way can efficiently treat highly conflicting evidence and has good convergence performance.