Analysis of the scalability of UWB indoor localization solutions for high user densities

Radio frequency (RF) technologies are often used to track assets in indoor environments. Among others, ultra-wideband (UWB) has constantly gained interest thanks to its capability to obtain typical errors of 30 cm or lower, making it more accurate than other wireless technologies such as WiFi, which normally can predict the location with several meters accuracy. However, mainly due to technical requirements that are part of the standard, conventional medium access strategies such as clear channel assessment, are not straightforward to implement. Since most scientific papers focus on UWB accuracy improvements of a single user, it is not clear to which extend this limitation and other design choices impact the scalability of UWB indoor positioning systems. We investigated the scalability of indoor localization solutions, to prove that UWB can be used when hundreds of tags are active in the same system. This paper provides mathematical models that calculate the theoretical supported user density for multiple localization approaches, namely Time Difference of Arrival (TDoA) and Two-Way Ranging (TWR) with different MAC protocol combinations, i.e., ALOHA and TDMA. Moreover, this paper applies these formulas to a number of realistic UWB configurations to study the impact of different UWB schemes and settings. When applied to the 802.15.4a compliant Decawave DW1000 chip, the scalability dramatically degrades if the system operates with uncoordinated protocols and two-way communication schemes. In the best case scenario, UWB DW1000 chips can actively support up to 6171 tags in a single domain cell (no handover) with well-selected settings and choices, i.e., when adopting the combination of TDoA (one-way link) and TDMA. As a consequence, UWB can be used to simultaneously localize thousands of nodes in a dense network. However, we also show that the number of supported devices varies greatly depending on the MAC and PHY configuration choices

The Future of the Operating Room: Surgical Preplanning and Navigation using High Accuracy Ultra-Wideband Positioning and Advanced Bone Measurement

This dissertation embodies the diversity and creativity of my research, of which much has been peer-reviewed, published in archival quality journals, and presented nationally and internationally. Portions of the work described herein have been published in the fields of image processing, forensic anthropology, physical anthropology, biomedical engineering, clinical orthopedics, and microwave engineering. The problem studied is primarily that of developing the tools and technologies for a next-generation surgical navigation system. The discussion focuses on the underlying technologies of a novel microwave positioning subsystem and a bone analysis subsystem. The methodologies behind each of these technologies are presented in the context of the overall system with the salient results helping to elucidate the difficult facets of the problem. The microwave positioning system is currently the highest accuracy wireless ultra-wideband positioning system that can be found in the literature. The challenges in producing a system with these capabilities are many, and the research and development in solving these problems should further the art of high accuracy pulse-based positioning

XRLoc: Accurate UWB Localization for XR Systems

Understanding the location of ultra-wideband (UWB) tag-attached objects and people in the real world is vital to enabling a smooth cyber-physical transition. However, most UWB localization systems today require multiple anchors in the environment, which can be very cumbersome to set up. In this work, we develop XRLoc, providing an accuracy of a few centimeters in many real-world scenarios. This paper will delineate the key ideas which allow us to overcome the fundamental restrictions that plague a single anchor point from localization of a device to within an error of a few centimeters. We deploy a VR chess game using everyday objects as a demo and find that our system achieves $2.4$ cm median accuracy and $5.3$ cm $90^\mathrm{th}$ percentile accuracy in dynamic scenarios, performing at least $8\times$ better than state-of-art localization systems. Additionally, we implement a MAC protocol to furnish these locations for over $10$ tags at update rates of $100$ Hz, with a localization latency of $\sim 1$ ms