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