Ultra-Wideband (UWB) systems are becoming increasingly popular for indoor localization, where range measurements are obtained by measuring the time-of-flight of radio signals. However, the range measurements typically suffer from a systematic error or bias that must be corrected for high-accuracy localization. In this paper, a ranging protocol is proposed alongside a robust and scalable antenna-delay calibration procedure to accurately and efficiently calibrate antenna delays for many UWB tags. Additionally, the bias and uncertainty of the measurements are modelled as a function of the received-signal power. The full calibration procedure is presented using experimental training data of 3 aerial robots fitted with 2 UWB tags each, and then evaluated on 2 test experiments. A localization problem is then formulated on the experimental test data, and the calibrated measurements and their modelled uncertainty are fed into an extended Kalman filter (EKF). The proposed calibration is shown to yield an average of 46% improvement in localization accuracy. Lastly, the paper is accompanied by an open-source UWB-calibration Python library, which can be found at https://github.com/decargroup/uwb_calibration.

翻译：超宽带（UWB）系统在室内定位中日益普及，其通过测量无线电信号的飞行时间获取距离测量值。然而，距离测量通常存在需校正的系统性误差或偏置，以实现高精度定位。本文提出一种测距协议，并配套稳健且可扩展的天线延迟校准流程，以准确高效地校准多个UWB标签的天线延迟。此外，将测量值的偏置和不确定度建模为接收信号功率的函数。利用3架各配备2个UWB标签的空中机器人的实验训练数据，展示了完整的校准流程，并在2项测试实验中进行评估。基于实验测试数据构建定位问题，将校准后的测量值及其建模不确定度输入扩展卡尔曼滤波器（EKF）。实验表明，所提校准方法使定位精度平均提升46%。最后，本文提供配套的开源UWB校准Python库，详见 https://github.com/decargroup/uwb_calibration。