Real-Time Pose Prediction and Dynamic Anchor Selection for Ultra-Wideband Tagless Gate
Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10869476/ |
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| Summary: | Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. In addition, the location of the user and MD might be different because of the pose of MD. Therefore, the knowledge about exact location and the pose of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. Based on the channel impulse response of UWB, DynaPose classifies the signal condition, such as line-of-sight (LOS) and non-LOS (NLOS), using deep learning models. To calculate the exact location of MD, DynaPose selects LOS signals to minimize the localization error from NLOS signals. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, and ultimately detects four different poses with an accuracy of 0.961 in real-time. Finally, the localization accuracy is achieved by 2.23 cm and 17.86 cm in static and dynamic scenarios, respectively. |
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| ISSN: | 2169-3536 |