4.3 Article

Random Permutation Set

出版社

CCC PUBL-AGORA UNIV
DOI: 10.15837/ijccc.2022.1.4542

关键词

Dempster-Shafer evidence theory; Permutation number; Random permutation set; Permutation mass function; Orthogonal sum; Random finite set; Threat assessment

资金

  1. National Natural Science Foundation of China [30400067, 60874105, 61174022, 61573290, 61973332]
  2. Program for New Century Excellent Talents in University [NCET-08-0345]
  3. Shanghai Rising-Star Program [09QA1402900]
  4. Chongqing Natural Science Foundation [2010BA2003]
  5. JSPS Invitational Fellowships for Research in Japan

向作者/读者索取更多资源

This paper explores the meaning of the power set in evidence theory and proposes a possible explanation of the power set based on Pascal's triangle and combinatorial number. It introduces a new kind of set called random permutation set (RPS), which consists of permutation event space (PES) and permutation mass function (PMF). The paper also discusses and summarizes the comparisons of probability theory, evidence theory, and RPS, and presents an RPS-based data fusion algorithm, which is applied in threat assessment and proves to effectively handle uncertainty.
For exploring the meaning of the power set in evidence theory, a possible explanation of power set is proposed from the view of Pascal's triangle and combinatorial number. Here comes the question: what would happen if the combinatorial number is replaced by permutation number? To address this issue, a new kind of set, named as random permutation set (RPS), is proposed in this paper, which consists of permutation event space (PES) and permutation mass function (PMF). The PES of a certain set considers all the permutation of that set. The elements of PES are called the permutation events. PMF describes the chance of a certain permutation event that would happen. Based on PES and PMF, RPS can be viewed as a permutation-based generalization of random finite set. Besides, the right intersection (RI) and left intersection (LI) of permutation events are presented. Based on RI and LI, the right orthogonal sum (ROS) and left orthogonal sum (LOS) of PMFs are proposed. In addition, numerical examples are shown to illustrate the proposed conceptions. The comparisons of probability theory, evidence theory, and RPS are discussed and summarized. Moreover, an RPS-based data fusion algorithm is proposed and applied in threat assessment. The experimental results show that the proposed RPS-based algorithm can reasonably and efficiently deal with uncertainty in threat assessment with respect to threat ranking and reliability ranking.

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