  In this paper, partition-based distributed state estimation of general linear
systems is considered. A distributed moving horizon state estimation scheme is
developed via decomposing the entire system model into subsystem models and
partitioning the global objective function of centralized moving horizon
estimation (MHE) into local objective functions. The subsystem estimators of
the distributed scheme that are required to be executed iteratively within each
sampling period are designed based on MHE. Two distributed MHE algorithms are
proposed to handle the unconstrained case and the case when hard constraints on
states and disturbances, respectively. Sufficient conditions on the convergence
of the estimates and the stability of the estimation error dynamics for the
entire system are derived for both cases. A benchmark reactor-separator process
example is introduced to illustrate the proposed distributed state estimation
approach.
