A new evidential similarity measurement based on Tanimoto measure and its application in multi-sensor data fusion. (September 2021)

• Record Type: Journal Article
• Authors: Deng, Zhan; Wang, Jianyu
• Is Part Of: Engineering applications of artificial intelligence. Volume 104(2021)
• Publication Date: 2021-09
• Subjects: Dempster–Shafer evidence theory -- Conflict measurement -- Evidential similarity measurement -- Evidential conflict measurement -- Target recognition; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2021.104380
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Dempster–Shafer evidence theory is a powerful fusion approach for reasoning under uncertain circumstances. However, unreasonable results will generate when utilizing Dempster's combination rule to fuse highly conflicting evidence. How to accurately measure the conflict degree between two pieces of evidence is still a problem to be solved. To date, the method of conflict measurement has been proposed by many authors. Unfortunately, the effectiveness of these approaches is limited. In this paper, a new method of evidential similarity measurement is devised in the light of Tanimoto measurement to describe the inconsistency between bodies of evidence. Then, a novel evidential conflict measurement approach is presented in accordance with the evidential similarity measurement to quantify the conflict degree between bodies of evidence. It is analyzed and proved that the novel approach of evidential conflict measurement satisfies the property of conflict measures. Some numerical examples are given to demonstrate the efficacy and superiority of the proposed approach. Moreover, the validity of the proposed approach is evaluated by the application of target recognition.