An evidence fusion method using generalized Mahalanobis distance in Dempster-Shafer Theory

For decades, Dempster-Shafer evidence theory provides a solution for information fusion with reduced uncertainty and ambiguity. However, it commonly has to suffer from the counter-intuitive result. This paper presents a new approach by combining the generalized Mahalanobis distance with the theory, which assures the computation of evidences´ dissimilarities be calculable. The experiment demonstrates that the proposed method outperforms other techniques with a more reasonable aggregation.