An Adaptive Kalman Filter Combination Positioning Method Integrating UWB and GPS

Accurate and stable dynamic target positioning can provide high-precision vehicle motion state information for the autonomous driving decision-making system, which plays a very important role in the development of autonomous driving technology. In order to further improve the accuracy and stability of dynamic target positioning, based on the combination of Global Positioning System (GPS) and Ultra Wide Band (UWB) ranging information, the Kalman filter (KF) algorithm is used to process the measurement data of dynamic target. Since it is difficult to determine the system model noise and measurement noise under dynamic conditions, this paper introduces adaptive factor and dynamic window adjustment based on the covariance matching method, then proposes an adaptive Kalman filter (AKF) combination positioning method integrating UWB and GPS. In this method, the GPS single point positioning based on the sliding window AKF algorithm of covariance matching is used when the target is in the linear motion state. Under the condition of nonlinear motion of the target, the UWB/GPS integrated location is on account of the adaptive unscented Kalman filter (AUKF) algorithm which imports the filter divergence criterion. The simulation results confirm that the combined positioning method can adapt to the dynamic positioning requirements in complex environment and outperforms the traditional KF. The developed filtering algorithm has high adaptability, strong robustness, and can effectively improve the positioning accuracy.