A hierarchical adaptive information fusion method based on multimodal kalman filtering

In the multi-sensor information fusion tracking system, there are many uncertain or happens often unforeseen changes in environmental factors, if does not consider these factors in the design of the fusion algorithm, in the practical application may leads to the fusion system accuracy decreased or even complete failure. Therefore we must design a system which can adjust the algorithm adaptively. In this paper, the author will for sensors in the system the number or types of changes caused by the model change, puts forward a multiple model Kalman filter based adaptive fusion algorithm, and has carried on the simulation analysis, this method not only improves the flexibility and fault tolerance of the system, and the full integration of the complementary information and redundant information, and the method is simple, strong versatility.