ULoc: Low-Power, Scalable and cm-Accurate UWB-Tag Localization and Tracking for Indoor Applications
Minghui Zhao, Tyler Chang, Aditya Arun, Roshan Sai Ayyalasomayajula, Chi Zhang, Dinesh Bharadia
摘要
A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.
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引用它的顶会 Paper10
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- Rethinking WiFi-based Angle Estimation for Robust Passive Indoor LocalizationWenwei Li, Jiarun Zhou, Jie Xiong, Yuhui Xie 等UbiComp 2026 · 被引用 9 次
它引用的顶会 Paper3
- Deep learning based wireless localization for indoor navigationRoshan Sai Ayyalasomayajula, Aditya Arun, Chenfeng Wu, Sanatan Sharma 等MobiCom 2020 · 被引用 221 次
- TagFi: Locating Ultra-Low Power WiFi Tags Using Unmodified WiFi InfrastructureElahe Soltanaghaei, Adwait Dongare, Akarsh Prabhakara, Swarun Kumar 等UbiComp 2021 · 被引用 31 次
- Acoustic Strength-based Motion TrackingLinfei Ge, Qian Zhang, Jin Zhang, Qianyi HuangUbiComp 2021 · 被引用 13 次
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