Robust Explosion Point Location Detection via Multi–UAV Data Fusion: An Improved D–S Evidence Theory Framework

作者：Xuebin Liu, Hanshan Li · 发表于：Mathematics · 年份：2025 · DOI：10.3390/math13243997 · 研究领域：Adversarial Robustness in Machine Learning、Military Defense Systems Analysis、Target Tracking and Data Fusion in Sensor Networks

The Dempster–Shafer (D–S) evidence theory, while powerful for uncertainty reasoning, suffers from mathematical limitations in high–conflict scenarios where its combination rule produces counterintuitive results. This paper introduces a reformulated D–S framework grounded in optimization theory and information geometry. We rigorously construct a dynamic weight allocation mechanism derived from minimizing systemic Jensen–Shannon divergence and propose a conflict–adaptive fusion rule with theoretical guarantees. We formally prove that our framework possesses the Conflict Attenuation Property and Robustness to Outlier Evidence. Extensive Monte Carlo simulations in multi–UAV explosion point localization demonstrate the framework’s superiority, reducing localization error by 75.6% in high–conflict scenarios compared to classical D–S. This work provides not only a robust application solution but also a theoretically sound and generalizable mathematical framework for multi–source data fusion under uncertainty.