Sequential filtering for state constrained systems with cross-correlated measurement noises

This paper deals with the data fusion problem for the state constrained systems with correlated measurement noises. The purpose is to present a new optimal sequential decentralized filtering algorithm. Using the constraint information, the original state equation is transferred into a dimension reduction system without constraint, and new measurement outputs with independent noises is obtained based on the successive orthogonalization of the original cross-correlated noises. Then the sequential filter of the dimension reduction state is presented and the estimate of the full state is also reconstructed. A numerical example is presented to demonstrate the effectiveness of the method.