Adaptive robust UKF for nonlinear systems with parameter uncertainties
Shinji Ishihara, Masaki Yamakita · 2016
This paper addresses robust filtering for nonlinear systems with parameter uncertainties. We developed a new robust unscented Kalman filter (RUKF) which doesn't require calculating Jacobian matrix by using Unscented Statistical Linearization to consider the influence of parameter uncertainties of covariance matrices. The RUKF is more accurate than conventional UKF when the systems have parameter uncertainties. However, when there is no parameter uncertainty, estimation accuracy of the RUKF may be inferior to that of the UKF. Then, we also developed adaptive RUKF (ARUKF) by introducing an adaptive scheme into RUKF to automatically tune the influence of parameter uncertainties. The validity of the proposed methods is illustrated by Monte Carlo simulations.