An Algorithm of Data Fusion Using Artificial Neural Network and Dempster-Shafer Evidence Theory

Abstract

A new algorithm of data fusion using neural networks and Dempster-Shafer (D-S) evidence theory is presented in this paper to overcome these faults of data fusion, i.e., low accurate identification, bad stabilization and solution of uncertainty in some ways under multi-sensor environment. In this paper, according to the characteristic of the information obtained from multi-sensor obtained, firstly we divide obtained features into some groups and set up corresponding neural network to every group, meanwhile we introduce a concept of unknown probability to the goals based on the result of credible probability of these goals, secondly we have a fusion of time and space depending on the transpositional result of the neural networks’ output by D-S evidence theory. This method has the advantage of both neural and D-S evidence theory, and solves the problem that the general ways of data fusion can not identify the multi-sensor’s uncertainty information of great noise at present. At last simulation shows that the method can effectively improve the rate of the targets’ identification and keep great antinoise capacity.