Based on D-S evidence theory of information fusion improved method

Abstract

With the rapid development of information technology, multi-sensor information fusion research has been made great progress, currently there are Bayesian test, the voting method, fuzzy sets, neural networks and other methods algorithm being used in multi-sensor information fusion commonly. However, there are many shortcomings which are difficult to overcome in the process of fusion by these methods, so now the international community has already proposed to apply the D-S evidence theory to information fusion. This method does not require the priori probability, and it's very useful when it is used in information fusion, but the information with the conflict can not be effectively fused by using D-S evidence theory. Larger conflict in the evidence, the fusion result is more easily prone to false consensus. Therefore, this paper presents an improved information fusion method based on original D-S evidence theory, and this method can deal with the evidences with conflict effectively. In this paper, the improvement is in the denominator of the D-S evidence theory. When the evidence is purely of conflict, the denominator is zero, the formula does not make sense. This improvement thinking is coupled with sum between the orthogonal sum of all of evidence and product, which is between the conflict factor and the inverse value of the standard deviation of the support degree, and then it subtract maximum difference of the basic probability of the evidence. If the conflict of the evidence is greater, this advantage of this method is more obvious. If there is no conflict in the evidence, the fusion result is consistent with item of original D-S evidence theory. Finally the simulation experiment gives a set of data with more conflict. The comparative data from the experiments show that the improved method of information fusion is essential to improved in resolving conflict issues, and it is effective.