A new divergence measure for belief functions and its applications

Information fusion in uncertain and complex environments is highly challenging. Dempster–Shafer (D-S) evidence theory has been successfully applied by various researchers in multi-sensor data fusion. However, it yields counterintuitive results in case of highly conflicting evidence. In this paper, we have developed a new divergence measure for belief functions that is nonnegative, symmetric, and satisfies the triangle inequality. Using the developed divergence measure, an algorithm for combining distinct basic probability assignments (BPAs) has been discussed and applied in target recognition systems and classification problems.