An Improved Method for Combining Conflicting Evidences Based on the Similarity Measure and Belief Function Entropy

Abstract

Dempster–Shafer evidence theory is widely adopted in a variety of fields of information fusion. Nevertheless, it is still an open issue about how to avoid the counter-intuitive results to combine the conflicting evidences. In order to overcome this problem, an improved conflicting evidence combination approach based on similarity measure and belief function entropy is proposed. First, the credibility degree of the evidences and their corresponding globe credibility degree are calculated on account of the modified cosine similarity measure of the basic probability assignment. Next, according to the globe credibility degree of the evidences, the primitive evidences are divided into two categories, namely, the reliable evidences and the unreliable evidences. In addition, for strengthening the positive effect of the reliable evidences and alleviating the negative impact of the unreliable evidences, a reward function and a penalty function are designed, respectively, to measure the information volume of the different types of the evidences by taking advantage of the Deng entropy function. Then, the weight value that obtained from the first step is modified by making use of the measured information volume. Finally, the modified weights of the evidences are applied for adjusting the body of the evidences before using the Dempster’s combination rule. A numerical example is provided to illustrate that the proposed method is reasonable and efficient in dealing with the conflicting evidences with better convergence. The results show that the proposed method is not only efficient, but also reliable. It outperforms other related methods which can recognise the target more accurate by 98.92%.