NLOS occlusion UWB TOA ranging dataset in indoor environment

we propose a novel Non-Line-of-Sight (NLOS) identification and error-mitigation method for dynamic object positioning and ultra-wideband (UWB) ranging. By applying inverse estimation on known Anchor Points (Aps) and improved unscented Kalman filter (IRUKF), the proposed technology identifies and compensates for NLOS occlusions between tag and APs, reducing positioning errors. The approach has been verified through simulation and experiment, with identification precision of 97.02%. After mitigating errors, we observed significant error reductions of 91.80%, 98.90% in Line-of-sight (LOS), NLOS situations, respectively. Moreover, the developed IRUKF algorithm effectively minimizes mislocalization by 50.48% in harsh scenarios. This data is collected by IMCM laboratory, including "Z", "U", "O" three dynamic tracking positioning trajectories

本提案提出了一种新颖的非视距（NLOS）识别与误差补偿方法，旨在动态目标定位及超宽带（UWB）测距。通过在已知的锚点（Aps）上实施逆估计以及改进的无迹卡尔曼滤波（IRUKF），该技术能够识别并补偿标签与锚点之间的NLOS遮挡，从而降低定位误差。该方法已通过仿真和实验得到验证，其识别精度高达97.02%。在误差补偿后，我们观察到视距（LOS）和非视距（NLOS）情况下的误差分别显著降低了91.80%和98.90%。此外，开发的IRUKF算法在恶劣场景中有效降低了定位误差，误差减少率高达50.48%。该数据由IMCM实验室收集，包括“Z”、“U”、“O”三种动态跟踪定位轨迹。