Ultra-wide band (UWB) technology has been widely used in indoor positioning,intelligent transportation and other fields. However,due to the obstructions or other factors,UWB ranging can have errors,which greatly reduces the positioning accuracy and reliability. This paper analyzed the characteristics of the robust Kalman filter (RKF),and improved a RKF-SW (sliding window,SW)for gross error detection. Initial outlier detection was performed based on the prediction residual,and an 1/4 sample quantile was used for subsequent sample outlier detection. Compared with the RKF,the RKF-SW had strong outlier identification capabilities and improved the positioning accuracy. In open scenes,the positioning accuracy was increased by approximately 80.0％,and in the case of interference from obstructions,the positioning accuracy was increased by approximately 82.1％.
outlierssample quantilerobust estimationultra-wide band (UWB)
NETL
NSTL
万方数据
