Improved State Estimation using a Combination of Moving Horizon Estimator and Particle Filters

State estimation is an important part of advanced process control. A moving horizon estimator (MHE) is often used for state estimation due to its robustness and ease of handling constraints. Sequential Monte-Carlo type techniques for state estimation also called particle niters (PF) are becoming popular due to their speed and ease of implementation. In this paper we present a novel combination of the MHE with the PF to gives a robust fast state estimator. The combined advantages of the MHE and particle filter provide efficient state estimation.