A range error reduction technique for positioning applications in sports

Abstract

In recent times, ultra-wideband (UWB)-based positioning systems have become popular in sport performance monitoring. UWB positioning system uses time of arrival to calculate the range data between devices (i.e. anchors, tags), and then use trilateration algorithms to estimate position coordinates. In practical applications, non-line-of-sight transmissions and multipath propagations lead to inaccurate range data and lower positioning accuracy. This paper introduces a range error minimisation algorithm to address this limitation of error in range data in UWB-based positioning system. The proposed solution analyses the range error for each anchor and sequentially reduces this error based on the distance between each anchor and the tag. This ultimately contributes to higher positioning accuracies. The authors implemented the proposed algorithm in a hardware test-bed, evaluating the positioning accuracy for an indoor sport. The proposed algorithm outperforms both the trilateration algorithm and a commercially used positioning algorithm by up to 50% and 25%, respectively. How to cite this article: Waqar A, Ahmad I, Habibi D, Phung QV. A range error reduction technique for positioning applications in sports.

High speed moving object location based on UWB ranging calibration

“…Most researches raise the positioning accuracy by improving the ranging methods. For example, the literature [4] proposed a co-location combining DS-TWR and TDOA, Waqar proposed a distance error minimization algorithm to analyze the error of each anchor point [5] , Fan proposed to combine the weighted least squares and Kalman filter to solve the distance deviation caused by NLOS error [6] . Filter processing and algorithm fusion are adopted for the motion target, For example, Zhenjie proposed a distance estimation based on motion state recognition [7] , Siquan proposed a TWRbased UWB method to solve the ranging problem between moving vehicles [8] .Qinghua, Li proposed particle filter to process ranging data, and used weighted least squares method to locate objects [9] .…”