Information Fusion Of Several Algorithms

In recent years, multi-sensor information fusion has attracted more and more attention. It has also been applied to many fields. At present, many military powers have invested large numbers of manpower, financial and material resources in research of information fusion. They arranged many projects and have gained a large mount of satisfying results. China also regards information fusion as one of the key technologies in new and hi-tech areas such as computer technology and spatial technology.Information fusion is such a kind of theories and methods which can process information from different sources. By synthetically treatment with these information, multi-sensor information fusion can produce much more exact presentation of real environment. Essentially, multi-sensor information fusion is just an issue of parameter estimation, or an issue of algorithm. So it is of great importance to lucubrate information algorithm. So this paper does detailed research on the results of domestic and foreign papers.First, this paper introduces definition, system structures, and applications of multi-sensor information fusion. Then, through examples and simulations, this paper particularly studies the applications of methods such as Bayes estimation theory, Dempster-Shafer evidence theory, artificial neural network, Rough Set theory and Fuzzy Integral. It also makes more efforts in a new information fusion method that combines rough set theory and neural network. From the work mentioned above, the paper independently gives a new method to generate belief functions based on rough set. And it is accordant with the requirement of Dempster-Shafer evidence theory.The efforts done in this paper provides a good reference for further study on multi-sensor fusion.