Evidential Information Fusion on Possibilistic Structure

TL;DR

This paper introduces a flexible evidential information fusion framework based on possibilistic structures and triangular norms, overcoming limitations of Dempster's rule in complex and diverse source scenarios.

Contribution

It proposes a reversible transformation between belief functions and possibilistic structures, enabling more adaptable and effective evidence combination methods.

Findings

Supports more flexible combination behaviors

Improves conflict management in evidence fusion

Enhances heterogeneous information integration

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.