Globally optimal distributed Kalman filtering for multisensor systems with unknown inputs

In this paper, we consider the state estimation for multisensor dynamic systems with unknown inputs. we proposed an optimal distributed algorithm in mean square error sense. It was proved that the globally optimal distributed fusion algorithm is equivalent to the centralized Kalman filtering using all sensor measurement. Thus, the systems has not only the global optimality, but also the good survivability in a poor situation. Moreover, the new algorithm shows good performance with much less computations compared to that of the traditional augmented state algorithms. Numerical example is given to support our analysis.