COMPUTATIONALLY EFFICIENT OPTIMAL OUTPUT DECENTRALIZED ESTIMATION

This paper presents decentralized computational architectures for the optimal state estimation in stochastic large-scale linear systems. The main feature of the proposed architectures lies on elaborating each subsystem's decision, not only by processing its own local data, but also by adjusting this decision with all other related subsystem's local data. This adjustment procedure ensures the optimality of the decentralized filter. ft is emphasized that the Kalman filter algorithm operates more efficiently when measurements are processed into low-order subsets, especially when they are processed one at a time. Thus, using this feature in a decentralized scheme significantly increases computational savings and numerical stability. Architectures presented in this paper for the mechanization of decentralized estimators allow a high degree of parallelism and can be implemented on a wide range of computer networks.