The key highlights and insights of the content are:
The authors formulate a linear full-information estimation design within a distributed framework as the basis for developing their approach.
They establish the analytical solution to the local optimization problems associated with the distributed full-information design in the form of a recursive distributed Kalman filter algorithm.
The linear distributed Kalman filter is then extended to the nonlinear context by incorporating successive linearization of nonlinear subsystem models, resulting in the proposed distributed extended Kalman filter approach.
Rigorous analysis is conducted, and the stability of the estimation error dynamics provided by the proposed method for general nonlinear processes is proven.
The effectiveness of the proposed method is illustrated through a chemical process example and its application to a wastewater treatment process with 145 state variables.
The proposed partition-based distributed extended Kalman filter addresses the limitations of existing distributed extended Kalman filter algorithms, enabling scalable and efficient state estimation for large-scale nonlinear processes.
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