A generalized measure of basic probability assignment uncertainty and its application in evidence combination

被引：0
|
作者：
Yu S. [1 ]
Wang X. [1 ,2 ]
机构：
[1] Department of Automation, Heilongjiang University, Heilongjiang, Harbin
[2] Key Laboratory of Information Fusion Estimation and Detection in Heilongjiang Province, Heilongjiang, Harbin
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2024年 / 41卷 / 03期
基金：
中国国家自然科学基金;
关键词：
D–S evidence theory; evidence combination; generalized measure of uncertainty; improved normalized projection;
D O I：
10.7641/CTA.2023.20845
中图分类号：
学科分类号：
摘要：
Measurement of the uncertainty of the basic probability assignment (BPA) given by the sensor is a key issue in the D–S (dempster-shafer) evidence theory, and the accuracy level of the uncertainty is crucially important to assess the quality of information conveyed by belief structures. This paper first proposes an improved normalized projection method (iNP), and then presents a new generalized measure of projection uncertainty (PU) based on iNP. Theoretical proofs and experimental simulations illustrate that PU satisfies the properties of nonnegativity, boundness, invariance, monotonicity, non-reverse intuition, higher sensitivity and lower computational burden, which ensure that PU can effectively realize generalized measure of BPA uncertainty. Compared with the existing uncertainty measures, the proposed method is more sensitive to changes in evidence. Finally, a new evidence combination method based on the PU method is put forward, and the effectiveness and the rationality of the proposed method are illustrated by numerical examples and practical applications. © 2024 South China University of Technology. All rights reserved.
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收藏
页码：567 / 576
页数：9
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