A Fusion Algorithm of Multisensor System Based on Sequential Filtering in Distributed Network

This paper develops a data fusion algorithm of multisensor system based on sequential filtering in distributed network. In actual multisensor dynamic system, in every time- step, observations obtained from local sensors are transformed to the center coordinate system. And then, they are transmitted to the center processor in turn. In the center processor, observations are used to update sequentially the previous global estimate with Kalman filters. Thus one can obtain a global estimate of the state at this step based on the global information. In this paper, the deductive process of the algorithm is presented. On the condition of the same accuracy , we compare the computational cost of new algorithm with that of the centralized data fusion algorithm to show the superiority of the former.Finally the simulation indicates the validity of the algorithm.