A new divergence measure for belief functions in D–S evidence theory for multisensor data fusion

Abstract

Dempster–Shafer (D–S) evidence theory is useful for handling uncertainty problems in multisensor data fusion. However, the question of how to handle highly conflicting evidence in D–S evidence theory is still an open issue. In this paper, a new reinforced belief divergence measure, called RB, is developed to measure the discrepancy between basic belief assignments (BBAs) in D–S evidence theory. The proposed RB divergence is the first such measure to consider the correlations between both belief functions and subsets of the sets of belief functions, thus allowing it to provide a more convincing and effective solution for measuring the discrepancy between BBAs. Additionally, the RB divergence has certain benefits in terms of measurement. In particular, it has the properties of nonnegativeness, nondegeneracy, symmetry and satisfaction of the triangle inequality. Based on the RB divergence, an algorithm for multisensor data fusion is then designed. Through a comparative analysis, it is verified that the proposed method is more feasible and reasonable than previous methods for measuring the divergence between BBAs. Finally, the proposed algorithm is effectively applied to a real-world classification fusion problem.