2022/12/10 by Leo Botler, Botler, Leo, Konrad Diwold +3
Engineering · #FOS: Electrical engineering #Indoor and Outdoor Localization Technologies #Microwave Imaging and Scattering Analysis #Signal Processing (eess.SP) #Ultra-Wideband Communications Technology #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2212.05282
openalex publication_date 2022/12/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.