On signal strength-based distance estimation using UWB technology

TL;DR

This paper evaluates machine learning models for improving UWB signal strength-based distance estimation accuracy, demonstrating sub-decimeter precision in the same environment and promising results across different settings, with implications for cost-effective and secure indoor positioning.

Contribution

It introduces machine learning approaches for UWB signal strength-based distance estimation and proposes a method to resolve ambiguities in commercial transceivers, enhancing accuracy and security.

Findings

Sub-decimeter accuracy in same environment tests

Average MAE of 24 cm across different environments

Public availability of three experimental datasets

Abstract

Ultra-wideband (UWB) technology has become very popular for indoor positioning and distance estimation (DE) systems due to its decimeter-level accuracy achieved when using time-of-flight-based techniques. Techniques for DE relying on signal strength (DESS) received less attention. As a consequence, existing benchmarks consist of simple channel characterizations rather than methods aiming to increase accuracy. Further development in DESS may enable lower-cost transceivers to applications that can afford lower accuracies than those based on time-of-flight. Moreover, it is a fundamental building block used by a recently proposed approach that can enable security against cyberattacks on DE which could not be avoided using only time-of-flight-based techniques. In this paper, we evaluate the suitability of several machine-learning models trained in different real-world environments to increase UWB-based DESS accuracy. Additionally, aiming for implementation in commercial off-the-shelf (COTS) transceivers, we propose and evaluate an approach to resolve ambiguities comprising DESS in these devices. Our results show that the proposed DE approaches have sub-decimeter accuracy when testing the models in the same environment and positions in which they have been trained, and achieved an average MAE of 24 cm when tested in a different environment. 3 datasets obtained from our experiments are made publicly available.