Abstract
In the Dempster-Shafer evidence theory, how to effectively measure the degree of conflict between two bodies of evidence is still an open question. To solve this problem, we propose a weighted conflict evidence combination method based on Hellinger distance and the belief entropy. This method uses the probability transformation function to deal with the multi-subset focal elements firstly. Next, the Hellinger distance is introduced to measure the degree of conflict among the evidence. Moreover, improved belief entropy is also employed to quantify the uncertainty of the basic belief assignments. Further, Hellinger distance and the improved belief entropy are combined to construct the weight coefficient concerning evidence, and finally, the Dempster combination rule is used for fusion. The final fusion results of proposed method on fault diagnosis experiment and target recognition experiment are 0.9018 and 0.9895 respectively, apparently higher than that of other methods, revealing the advantages of the proposed method.
Highlights
M ULTI-SENSOR information fusion technology can effectively avoid the limitation of single sensor decision-making by processing and fusing the information obtained by multiple sensors
Experts and scholars have successively proposed some classic theories, such as fuzzy set theory proposed by Zadeh in 1965 [2], evidence theory proposed by Dempster in 1967 [3], developed evidence theory proposed by Shafer in 1976 [4], the rough set theory proposed by Pawlak in 1982 [5], and so on
Inspired by the idea of probability transformation function proposed by Ma & An [46], we combine Hellinger distance and Dempster-Shafer evidence theory to characterise the degree of conflict between basic belief assignments (BBAs)
Summary
M ULTI-SENSOR information fusion technology can effectively avoid the limitation of single sensor decision-making by processing and fusing the information obtained by multiple sensors. Dempster-Shafer evidence theory has the advantage of expressing "uncertain" and "unknown", so it can deal with uncertain and imprecise information flexibly At present, it has been widely used in many fields, such as fault diagnosis [14]–[17], target tracking [18]–[20], multiple attribute decision making [21]–[24], image processing [25], [26], medical diagnosis [27]–[29], risk analysis [30]–[34], and so on
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