Square-Root Unscented Kalman Filter for Vehicle Integrated Navigation
Liguo Zhang, Hai-Bo Ma, Yangzhou Chen · 2007
In view of that there exist some defects when the extended Kalman filter (EKF) is applied in nonlinear state estimations, the square-root unscented Kalman filter (SRUKF), as a new nonlinear filtering method, is introduced to instead of EKF for the state-estimation of the vehicle integrated GPS/DR navigation system. Compared with EKF, SRUKF not only improves the location precision and algorithmic stability greatly, but also avoids the calculating burden of Jacobin matrices. This data fusion algorithm based on SRUKF is easy to implement, and meets the requirements of low-cost and high precision. In order to test the validity of SRUKF, the two methods are used to estimate states of the vehicle integrated GPS/DR navigation systems. The results of simulation show that SRUKF is superior to EKF and is a more ideal nonlinear filtering method for the vehicle integrated GPS/DR navigation.