Abstract
We develop some decentralized fixed-interval smoothing algorithms which are realized by the two-filter form in linear discrete-time systems, where the decentralized estimation structure consists of a central processor and of two local processors. Two approaches based on local filtering and local smoothing are considered for solving the problems of decentralized smoothing, smoothing update, and real-time smoothing. It is then shown that the notion of decentralized Kalman filtering plays a significant role in the derivation of the algorithms for the former approach, and that the introduction of the Hamiltonian system in discrete-time immediately gives the solutions of the problems in the latter approach. Moreover, some characteristics of the algorithms arising from each approach are also discussed.
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