The authors propose a scalable and efficient partition-based distributed extended Kalman filter method for estimating the states of general nonlinear processes consisting of interconnected subsystems.
The core message of this article is to develop an iterative partition-based distributed moving horizon estimation (DMHE) scheme for linear systems, where the local estimators penalize both system disturbances and measurement noise to provide accurate and stable state estimates.