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 a 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. Therefore, the knowledge about the exact
location 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. The pose is defined as the actual location of MD on the human
body, which affects the localization accuracy. DynaPose is based on
line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for
anchor selection and pose prediction. Deep learning models use the UWB channel
impulse response and the inertial measurement unit embedded in the smartphone.
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, 62% higher DL-TDoA localization accuracy, and
ultimately detects four different poses with an accuracy of 0.961 in real-time.

该论文提出了一种名为 DynaPose 的动态锚点选择和姿势预测的方法，通过深度学习使用 UWB 信道脉冲响应和手机上的惯性测量单元，实现了对 DL-TDoA 定位的极高准确度和实时检测四种不同姿势的能力。