Evidential Information Fusion on Possibilistic Structure

2026-05-16

AI Summary

This work addresses the limitations of the classical Dempster combination rule, which is constrained by its reliance on intersection-based semantics and struggles with complex evidence sources and diverse fusion scenarios. The authors propose an invertible transformation grounded in the principle of equal plausibility, mapping belief functions onto a possibility structure defined over the power set. By integrating a belief evolution network to model subset relationships, they develop an adaptive evidence fusion framework centered on families of t-norms. This approach transcends the semantic constraints of Dempster’s rule and provides a unified mechanism for fusing non-independent sources, managing conflict, enabling parameterized combination design, and integrating heterogeneous information. Consequently, it significantly enhances the flexibility and applicability of evidence theory in complex and high-conflict environments.

Abstract

Dempster's rule is a fundamental tool for combining belief functions from distinct and reliable sources. However, its intersection-based semantics imposes strong structural restrictions, which limits its flexibility in handling complex source states and diverse information fusion scenarios. To overcome this limitation, we propose a reversible transformation, derived from the isopignistic principle, between belief functions and a possibilistic structure defined on the power set. In this transformation, the relationships among subsets are explicitly characterized by a belief evolution network, which provides a more flexible representation of evidential information beyond the conventional mass function structure. On this basis, we further introduce the triangular norm family to develop a general and adaptive evidential information fusion framework. Unlike fusion methods rooted in Dempster semantics, the proposed framework supports more flexible combination behaviors and exhibits advantages in non-distinct source fusion, conflict management, parametric combination design, and heterogeneous information fusion.

Problem

• Dempster's rule
• evidential information fusion
• possibilistic structure
• belief functions
• information fusion

Innovation

• possibilistic structure
• isopignistic principle
• belief evolution network
• triangular norm
• evidential fusion