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。

超宽带无标签门的动态锚点选择和实时姿态预测

Dynamic Anchor Selection and Real-Time Pose Prediction for  Ultra-wideband Tagless Gate

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink
communication or centralized computation makes downlink
time-difference-of-arrival (DL-TDOA) localization the most suitable for
large-scale industrial deployments. However, temporary or permanent obstacles
in the deployment region often lead to non-line-of-sight (NLOS) channel path
and signal outage effects, which result in localization errors. Prior research
has addressed this problem by increasing the ranging frequency, which leads to
a heavy increase in the user device power consumption. It also does not
contribute to any increase in localization accuracy under line-of-sight (LOS)
conditions. In this paper, we propose and implement a novel low-power
channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS
probability predictor based on a convolutional neural network (CNN), a dynamic
ranging frequency control module, and an IMU sensor-based ranging filter. Based
on the conducted experiments, we show that the proposed algorithm achieves 50%
higher accuracy in NLOS conditions while having 46% lower power consumption in
LOS conditions compared to baseline methods from prior research.

本文提出并实施了一种新颖的低功耗、适应信道特性的动态频率下行到达差异时间测距算法，通过基于卷积神经网络（CNN）的 NLOS 概率预测器、动态测距频率控制模块和基于 IMU 传感器的测距滤波器，实验证明该算法在 NLOS 条件下的准确度比先前研究的基准方法提高了 50%，在 LOS 条件下的功耗降低了 46%。