Conflict Management for Target Recognition Based on PPT Entropy and Entropy Distance

Shijun Xu, Yi Hou, Xinpu Deng, Kewei Ouyang, Ye Zhang and Shilin Zhou
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Shijun Xu: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Yi Hou: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Xinpu Deng: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Kewei Ouyang: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Ye Zhang: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Shilin Zhou: College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

Energies, 2021, vol. 14, issue 4, 1-25

Abstract: Conflicting evidence affects the final target recognition results. Thus, managing conflicting evidence efficiently can help to improve the belief degree of the true target. In current research, the existing approaches based on belief entropy use belief entropy itself to measure evidence conflict. However, it is not convincing to characterize the evidence conflict only through belief entropy itself. To solve this problem, we comprehensively consider the influences of the belief entropy itself and mutual belief entropy on conflict measurement, and propose a novel approach based on an improved belief entropy and entropy distance. The improved belief entropy based on pignistic probability transformation function is named pignistic probability transformation (PPT) entropy that measures the conflict between evidences from the perspective of self-belief entropy. Compared with the state-of-the-art belief entropy, it can measure the uncertainty of evidence more accurately, and make full use of the intersection information of evidence to estimate the degree of evidence conflict more reasonably. Entropy distance is a new distance measurement method and is used to measure the conflict between evidences from the perspective of mutual belief entropy. Two measures are mutually complementary in a sense. The results of numerical examples and target recognition applications demonstrate that our proposed approach has a faster convergence speed, and a higher belief degree of the true target compared with the existing methods.

Keywords: Dempster-Shafer evidence theory; basic probability assignment; belief entropy; pignistic probability transformation entropy; uncertainty measurement; entropy distance; conflict management; target recognition (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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