An Adaptive Unscented Kalman Filter for Dead Reckoning Systems
Santong Zhang · 2009
The sequential filtering of discrete time nonlinear systems in the presence of unknown noise statistical parameters or time varying noise parameters is studied in this paper. The Sage-Husa statistics estimator is introduced to unscented Kalman filter (UKF), then the online estimation of unknown covariance of noise is completed with recursive operations, a novel adaptive unscented Kalman filter (AUKF) is proposed. The feasibility of this method is proved with a simulating example of dead reckoning (DR) system, and it positioning precision outperforms UKF, extended Kalman filter (EKF).