Abstract
Ultrawideband (UWB) technology applied in indoor localization systems is able to offer centimeter-level error in position measurement and attracts increasing popularity in various solutions. Position estimation is calculated based on ranging measurements between mobile nodes and anchors at known locations. Most existing works employ UWB in positioning context and resort to nonline-of-sight optimization imposed by static indoor building layout. In tracking applications, the direct path of UWB propagation may be obstructed by humans (pedestrians), which leads to significant ranging errors. It is observed that the ranging errors vary with respect to the degree of obstruction imposed by the pedestrian. In this paper, a UWB ranging error model with respect to the human body shadowing effect is proposed and evaluated by extensive measurements and experiments. The error model is applied in a particle filter pedestrian tracking algorithm fusing heading information obtained from an iPhone 7 built-in gyroscope. The performance of the proposed model outperforms the state-of-the-art approaches and has achieved up to 41.91% reduction in mean 2-D position error compared to the trilateration-based method in a pedestrian tracking case study.
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