Improving Signal-Strength-based Distance Estimation in UWB Transceivers.

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 to DE which could not be avoided using only time-of-flight-based techniques. In this paper, we aim to benchmark the performance of machine-learning models when used to increase the accuracy of UWB-based DESS. Additionally, aiming for implementation in commercial off-theshelf (COTS) transceivers, we propose and evaluate an approach to resolve ambiguities compromising 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.