  In this work, an innovative data-driven moving horizon state estimation is
proposed for model dynamic-unknown systems based on Bayesian optimization. As
long as the measurement data is received, a locally linear dynamics model can
be obtained from one Bayesian optimization-based offline learning framework.
Herein, the learned model is continuously updated iteratively based on the
actual observed data to approximate the actual system dynamic with the intent
of minimizing the cost function of the moving horizon estimator until the
desired performance is achieved. Meanwhile, the characteristics of Bayesian
optimization can guarantee the closest approximation of the learned model to
the actual system dynamic. Thus, one effective data-driven moving horizon
estimator can be designed further on the basis of this learned model. Finally,
the efficiency of the proposed state estimation algorithm is demonstrated by
several numerical simulations.
