{"title":"A new divergence measure for belief functions and its applications","authors":"Manpreet Kaur, Amit Kumar Srivastava","doi":"10.1080/03081079.2022.2151006","DOIUrl":null,"url":null,"abstract":"ABSTRACT Information fusion in uncertain and complex environments is highly challenging. Dempster–Shafer (D-S) evidence theory has been successfully applied by various researchers in multi-sensor data fusion. However, it yields counterintuitive results in case of highly conflicting evidence. In this paper, we have developed a new divergence measure for belief functions that is nonnegative, symmetric, and satisfies the triangle inequality. Using the developed divergence measure, an algorithm for combining distinct basic probability assignments (BPAs) has been discussed and applied in target recognition systems and classification problems.","PeriodicalId":50322,"journal":{"name":"International Journal of General Systems","volume":"52 1","pages":"455 - 472"},"PeriodicalIF":2.4000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of General Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1080/03081079.2022.2151006","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}
引用次数: 2
Abstract
ABSTRACT Information fusion in uncertain and complex environments is highly challenging. Dempster–Shafer (D-S) evidence theory has been successfully applied by various researchers in multi-sensor data fusion. However, it yields counterintuitive results in case of highly conflicting evidence. In this paper, we have developed a new divergence measure for belief functions that is nonnegative, symmetric, and satisfies the triangle inequality. Using the developed divergence measure, an algorithm for combining distinct basic probability assignments (BPAs) has been discussed and applied in target recognition systems and classification problems.
期刊介绍：
International Journal of General Systems is a periodical devoted primarily to the publication of original research contributions to system science, basic as well as applied. However, relevant survey articles, invited book reviews, bibliographies, and letters to the editor are also published.
The principal aim of the journal is to promote original systems ideas (concepts, principles, methods, theoretical or experimental results, etc.) that are broadly applicable to various kinds of systems. The term “general system” in the name of the journal is intended to indicate this aim–the orientation to systems ideas that have a general applicability. Typical subject areas covered by the journal include: uncertainty and randomness; fuzziness and imprecision; information; complexity; inductive and deductive reasoning about systems; learning; systems analysis and design; and theoretical as well as experimental knowledge regarding various categories of systems. Submitted research must be well presented and must clearly state the contribution and novelty. Manuscripts dealing with particular kinds of systems which lack general applicability across a broad range of systems should be sent to journals specializing in the respective topics.
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