An iterative partition-based moving horizon estimator for large-scale linear systems

We transfer the ideas behind sensitivity-driven distributed model predictive control (c.f. Scheu and Marquardt, 2011) to the moving horizon state estimation problem and present a novel decentralized state estimation algorithm, namely, sensitivity-driven partition-based moving horizon estimation (S-PMHE). We discuss convergence and optimality of S-PMHE for the case of given positive-definite arrival cost weights. Finally, we demonstrate the method on a numerical example.