An effective multi-source data fusion approach based on [formula omitted]-divergence in belief functions theory with applications to air target recognition and fault diagnosis

Abstract

Belief functions theory (BFT) plays a critical role in addressing uncertainty and imprecision in multi-source data fusion. Unfortunately, the application of Dempster’s rule in BFT often produces unexpected and counterintuitive results when it encounters highly conflicting evidence. In this paper, we propose an enhanced belief α-divergence to quantify the discrepancies between the evidences by considering both belief and plausibility functions. We further present two symmetric versions of enhanced belief α-divergence. We demonstrate that the proposed symmetric divergences exhibit key properties including non-negativity, non-degeneracy and symmetry. Also, we analyze that the proposed symmetric divergences can be converted to χ2 divergence, Jeffreys divergence, Hellinger distance, Jensen–Shannon divergence and arithmetic–geometric divergence under the framework of BFT in some special cases. Leveraging the proposed symmetric divergences, a novel multi-source data fusion approach is proposed, which evaluates the credibility and informational volume of each evidence, offering a more nuanced understanding of their significance. To validate the proposed approach, we apply it to air target recognition and fault diagnosis scenarios, where our proposed approach showcases superior performance.