High-Accuracy Ranging and Localization With Ultrawideband Communications for Energy-Constrained Devices

Abstract

Ultra-wideband (UWB) communications have gained popularity in recent years for being able to provide distance measurements and localization with high accuracy, which can enhance the capabilities of devices in the Internet of Things (IoT). Since energy efficiency is of utmost concern in such applications, in this work we evaluate the power and energy consumption, distance measurements, and localization performance of two types of UWB physical interfaces (PHYs), which use either a low-or high-rate pulse repetition (LRP and HRP, respectively). The evaluation is done through measurements acquired in identical conditions, which is crucial in order to have a fair comparison between the devices. We performed measurements in typical line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. Our results suggest that the LRP interface allows a lower power and energy consumption than the HRP one. Both types of devices achieved ranging and localization errors within the same order of magnitude and their performance depended on the type of NLOS obstruction. We propose theoretical models for the distance errors obtained with LRP devices in these situations, which can be used to simulate realistic building deployments and we illustrate such an example. This paper, therefore, provides a comprehensive overview of the energy demands, ranging characteristics, and localization performance of state-of-the-art UWB devices.

Managing Perceived Loneliness and Social-Isolation Levels for Older Adults: A Survey with Focus on Wearables-Based Solutions

“…The weights choice is a more qualitative problem, and it may require the social-psychology perspective in choosing it adequately. While the distances are today easily measured via positioning or proximity-detection sensors, as can be seen in recent examples in [ 85 , 86 , 87 , 88 ], the measurements of are less trivial and may require various underlying models and inter-dependencies of various parameters related to loneliness. Some of those parameters are discussed further in Section 4.4 and Table 2 , and a survey of possible sensors to measure and parameters is further given in Section 5 .…”

Self-Learning Detection and Mitigation of Non-Line-of-Sight Measurements in Ultra-Wideband Localization

“…On the other hand, the learned CIR features can also depend on the hardware of the end device. For instance, the CIR can have different shapes depending on the type of UWB device [15]. Therefore, models trained on features from one hardware model might not generalize well to others.…”

“…3 shows such an example, for a simulation of eight anchors when the tag and some anchors are separated by a wall. The wall introduces a lognormallydistributed error with a median of 24 cm and a standard deviation of 1.8 m (the parameters were obtained from a measurement campaign [15]). In many cases, the average residuals of LOS and NLOS anchors can be clearly delimited.…”

“…We also store the CIR corresponding to the selected measurement. Although we could simulate the distance errors for each obstacle based on proposed models [15], it is harder to simulate the CIR for a particular type of obstruction. Therefore, for a realistic setup, we preferred selecting the distance error and CIR of a real measurement from a database.…”

“…Therefore, for a realistic setup, we preferred selecting the distance error and CIR of a real measurement from a database. We use a database of distance measurements and CIRs acquired with UWB devices developed by 3db Access, which was partly described in [15]. The measurements were acquired in LOS, in NLOS with human body shadowing, or in NLOS with concrete wall shadowing at various indoor locations.…”