An algorithm of data fusion combined neural networks with DS evidential theory

A new algorithm of data fusion combined neural networks with DS evidential theory is presented to these questions of low accurate identification, bad stabilization and solution of uncertainty in some ways of multi-sensor system at present. According to the characteristic of characteristic information that the multi-sensor obtained, divide it into some groups and set up a corresponding neural network to every group, at the same time we introduce a concept of unknown probability to the goals based on the result of credible probability of these goals, at last we have a fusion of time and space depending on the transpositional result of the neural networks' output by DS evidential theory. The simulation shows that the way can effectively improve the rate of the targets' identification and great antinoise capacity.