Open AccessThe generalization negation of probability distribution and its application in target recognition based on sensor fusion
Author(s) -
Xiaozhuan Gao,
Yong Deng
Publication year - 2019
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1177/1550147719849381
Subject(s) - negation , computer science , probability distribution , entropy (arrow of time) , generalization , artificial intelligence , sensor fusion , information fusion , fusion , data mining , mathematics , statistics , linguistics , physics , quantum mechanics , mathematical analysis , programming language , philosophy
Target recognition in uncertain environments is a hot issue. Fusion rules are used to combine the sensor reports from different sources. In this situation, obtaining more information to make correct decision is an essential issue. Probability distribution is one of the most used methods to represent uncertainty information. In addition, the negation of probability distribution provides a new view to represent the uncertainty information. In this article, the existing negation of probability distribution is extended with Tsallis entropy. The main reason is that different systems have different parameter q. Some numerical examples are used to demonstrate the efficiency of the proposed method. Besides, the article also discusses the application of negation in target recognition based on sensor fusion to further demonstrate the importance of negation.
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