A Decentralized Computational Algorithm for the Global Kalman Filter

In this paper a new decentralized computational structure is developed for optimal state estimation in large scale linear interconnected dynamical systems. The new filter uses a hierarchical structure to perform successive orthogonalizations on the measurement subspaces of each subsystem in order to provide the optimal estimate. This ensures substantial savings in computation time. In addition, since only low-order subsystem equations are manipulated at each stage, numerical inaccuracies are reduced, and the filter remains stable for even high-order systems. This is illustrated on a multimachine example of a system comprising eleven interconnected machines. © 1978 IEEE