In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.

中文翻译：

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结合UWB测距估计和车载传感器的自动驾驶汽车的精确定位

在本文中，我们基于超宽带（UWB）测距估计以及车辆的里程表和惯性数据的融合来分析定位系统的性能。为了执行此数据融合，已使用扩展卡尔曼滤波器（EKF）。此外，已经设计了一种后处理算法来消除非视线（NLOS）UWB测距估计，以进一步提高所提出解决方案的准确性。该解决方案已经在模拟环境和真实环境中进行了测试。这项研究工作在PRoPART欧洲项目的范围内。已使用RISE（瑞典研究机构）开发的无线电遥控车（RC车）作为测试平台，在AstaZero试验场上进行了不同的实际测试。从而，