Compensating the NLoS Occlusion Errors of UWB for Pedestrian Localization With MIMU

The ultrawideband (UWB) technology has become a competitive choice for indoor pedestrian localization due to its advantages in accuracy, compact size, reliability, power efficiency, and easy setup. However, the none-line-of-sight (NLoS) occlusion caused by the human body parts and obstacles in its propagation path may introduce significant location errors that limit its performance and field of application. To handle the NLoS occlusion errors and promote the localization accuracy for complex indoor environments, a micro-electromechanical system (MEMS) inertial measurement unit (MIMU)-assisted UWB localization system with NLoS occlusion error compensation techniques is provided. First, an NLoS occlusion detection method using the built-in channel information of the UWB transceivers and the variation pattern of anchor-to-node distance is proposed. Second, an extended Kalman filter (EKF)-based attitude computation and a zero-velocity update (ZUPT)-based continuous gait segmentation are introduced for inertial navigation during occlusion intervals. Then, the UWB and IMU parameters are integrated with an unscented Kalman filter (UKF) framework to compensate for the occlusion error and improve the accuracy and robustness. Finally, a battery-powered miniature wearable device is developed and the proposed techniques are verified with experimental studies. The results demonstrate the feasibility and effectiveness of the presented techniques, which provides a good reference for related Internet of Things (IoT) applications.