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/decarsg/uwb_calibration.
翻译:在室内定位方面,对射频信号的飞行时间进行测量,从而获得测距测距的测距系统日益受到欢迎。不过,测距测量通常存在系统错误或偏差,必须加以纠正,以便高精度定位。在本文中,提出了一个测距协议,同时提出一个强力和可扩缩的天线误差校准程序,以准确和高效地校准许多世行标签的天线误差。此外,测距的偏差和不确定性是作为接收信号功率的函数来模拟的。全面校准程序是使用3个空中机器人的实验培训数据来进行的,这些机器人各装有2个UWB标签,然后对2个实验实验实验性实验性试验进行评价。然后,在实验性测试数据上提出定位问题,校准的测量及其模型不确定性被输入一个扩展的Kalman过滤器(EKEF)。拟议的校准显示,测距平均46%的地方化精确度提高46%。最后,本文附有一个开放源的UWB-校正平坦图书馆,可在 httpslimb_qubub.com/cardegusionalation.