Tuning of unscented Kalman filter based biogeography-based optimization for UGVs navigation

Abstract

In the case of linear systems, corrupted by white Gaussian noise, the Kalman filter is proved to be an optimal filter in the mean least square sense. When the system model and measurements are non-linear, variation of Kalman filter like extended Kalman filter (EKF), Unscented Kalman filters (UKF) and Particle filter (PF) are used. However, the best performance of UKF is achieved when the random variables only are Gaussian, and affected seriously by the estimation precision of noise covariance. In this paper, we give a noise covariance estimation method based Biogeography-based Optimization (BBO). The experimental results obtained from real-world road tests validate the performance of our approach. © 2011 Springer-Verlag.