Improved-Performance Vehicle’s State Estimator Under Uncertain Model Dynamics

Abstract

This article proposes an enhanced fusion technique to improve the accuracy of the state estimation of a navigational system. The smooth variable structure filter (SVSF) is examined to estimate the system’s state under model uncertainty. Its combination with the unscented Kalman filter (UKF) to acquire better navigational accuracy while being robust to the system’s modeling uncertainty is investigated. The proposed hybrid method is compared with the extended Kalman filter (EKF), the UKF, and the SVSF. The proposed algorithms fuse an inertial measurement unit (IMU) with the Global Positioning Systems (GPS) measurements to obtain the vehicle’s state. Experimental results are compared to a commercial off-the-shelf (COTS) solution. It is shown that all filtering strategies have similar performance in the absence of large-magnitude noise and model uncertainties. When injecting modeling uncertainties, the performance of the UKF degrades, and that of the EKF goes out of bounds. On the other hand, increasing the covariances of the measurement and dynamics noise sequences causes the path of the SVSF to become nonsmooth and roughly oscillates around the true path. The proposed integrated UK-SVSF algorithm achieves the following objectives: first, using the Kaman-based filter enhances the optimality of the filter to GPS/IMU dynamics and measurements noise. Second, using the UKF reduces the estimation error by eliminating the first-order linearization step. Finally, using the SVSF enhances the estimate’s robustness to model uncertainty. Results reveal that, in the presence of both large-magnitude noise and model uncertainties, the UK-SVSF gives an enhanced estimation performance.