Optimal vehicle position estimation using adaptive unscented Kalman filter based on sensor fusion

Abstract

Precise position recognition systems are actively used in various automotive technology fields such as autonomous vehicles, intelligent transportation systems, and vehicle driving safety systems. In line with this demand, this paper proposes a new vehicle position estimation algorithm based on sensor fusion between low-cost standalone global positioning system (GPS) and inertial measurement unit (IMU) sensors. In order to estimate accurate vehicle position information using two complementary sensor types, adaptive unscented Kalman filter (AUKF), an optimal state estimation algorithm, is applied to the vehicle kinematic model. Since this AUKF includes an adaptive covariance matrix whose value changes under GPS outage conditions, it has high estimation robustness even if the accuracy of the GPS measurement signal is low. Through comparison of estimation errors with both extended Kalman filter (EKF) and UKF, which are widely used state estimation algorithms, it can be confirmed how improved the estimation performance of the proposed AUKF algorithm in real-vehicle experiments is. The given test course includes roads of various shapes as well as GPS outage sections, so it is suitable for evaluating vehicle position estimation performance.