  In this paper we propose a novel partition-based distributed state estimation
scheme for non-overlapping subsystems based on Kalman filter. The estimation
scheme is designed in order to account, in a rigorous fashion, for dynamic
coupling terms between subsystems, and for the uncertainty related to the state
estimates performed by the neighboring subsystems. The online implementation of
the proposed estimation scheme is scalable, since it involves (i) small-scale
matrix operations to be carried out by the estimator embedded in each subsystem
and (ii) neighbor-to-neighbor transmission of a limited amount of data. We
provide theoretical conditions ensuring the estimation convergence.
Reconfigurability of the proposed estimation scheme is allowed in case of plug
and play operations. Simulation tests are provided to illustrate the
effectiveness of the proposed algorithm.
