  In this paper, we address a partition-based distributed state estimation
problem for large-scale general nonlinear processes by proposing a Kalman-based
approach. First, we formulate a linear full-information estimation design
within a distributed framework as the basis for developing our approach.
Second, the analytical solution to the local optimization problems associated
with the formulated distributed full-information design is established, in the
form of a recursive distributed Kalman filter algorithm. Then, the linear
distributed Kalman filter is extended to the nonlinear context by incorporating
successive linearization of nonlinear subsystem models, and the proposed
distributed extended Kalman filter approach is formulated. We conduct rigorous
analysis and prove the stability of the estimation error dynamics provided by
the proposed method for general nonlinear processes consisting of
interconnected subsystems. A chemical process example is used to illustrate the
effectiveness of the proposed method and to justify the validity of the
theoretical findings. In addition, the proposed method is applied to a
wastewater treatment process for estimating the full state of the process with
145 state variables.
