Gain constrained robust UKF for nonlinear systems with parameter uncertainties
Shinji Ishihara, Masaki Yamakita · 2016
This paper addresses the state estimation problem for nonlinear systems with parameter uncertainties. Firstly, we analyze the influence which the parameter uncertainties have on covariance matrix to a mean square error, and derived robust UKF (RUKF). The RUKF is less sensitive to deviations, and more accurate than conventional UKF. However, the estimated values of the RUKF can have some offsets by the influence of parameter uncertainties. Then we also analyze the influence which the parameter uncertainties have on predictive estimate, and develop new estimation gain to reduce the influence of the parameter uncertainties. We call this new robust filtering method as Gain-Constrained RUFK (GC-RUKF). The validity of the proposed methods is illustrated by Monte Carlo simulations.