Robust Localization for Mobile Targets Along a Narrow Path With LoS/NLoS Interference

Recently, with the development of automatic driving, high-precision localization has become indispensable for safety. The inertial measurement unit (IMU) integrated with an ultrawideband (UWB) technology has been thought as a promising scheme by the company Humatics for trains. Generally, the spaces of transportation are extended along a long path with flexural tunnels and sideway obstacles around, where Nonline-of-Sight (NLoS) propagation happens frequently. However, a large interval deployment of UWB anchors along such a narrow path with NLoS interference will dramatically decrease localization performance. Hence, we propose a robust algorithm to mitigate the NLoS interference. First, a joint LoS/NLoS detection and mitigation algorithm is proposed to improve the ranging accuracy of mobile target with UWB tags under mixed LoS/NLoS interference. Second, we improve the conventional Kalman Filter (KF) algorithm with forward–backward propagation to integrate temporal correlations further. Finally, a comprehensive fusion localization scheme with ranging error mitigation and improved KF is proposed. The simulation results show that, regarding the static localization, the proposed joint LoS/NLoS detection and mitigation algorithm outperforms five other typical NLoS elimination algorithms, and achieves 48.2%, 62.4%, 45.5%, 69.9%, and 50.2% gains in terms of root mean square error (RMSE), under NLoS scenarios with a mean ranging error of 0.5 m. Regarding the dynamic localization, compared with two other typical KF localization algorithms, the proposed localization scheme achieves 20.9% and 14.5% localization accuracy improvements in LoS scenarios, 45.7% and 47.2% improvements in mixed LoS/NLoS scenarios, respectively. Furthermore, the effectiveness of the proposed scheme is also verified in our testing platform.