An Optimal Sequential Decentralized Filter of Discrete-time Systems with Cross-Correlated Noises

Abstract In this paper, a new sequential decentralized computational structure is developed for optimal state estimation in discrete time-varying linear stochastic control system with multiple sensors and cross-correlated noises. We uses a hierarchical structure to perform successive orthogonualizations of the measurement noises, and the Kalman filters sequentially runs based on the new constructed measurement sequency. The the estimator also can process the system with measurements delay as well as data fault because the update step is just according to the coming order of sensors in a recursive form. The precision relation between the new algorithm and the centralized multisensor fusion method is strictly proved and simulation result shows that new filter is better than other similar filters in performance.

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