Map Inaccuracies Of Digital Twins For Localization

Abstract

Digital twins are increasingly vital in wireless communication for simulating, analyzing, and optimizing real-world environments, particularly for sensing and localization applications. The fidelity of these digital representations is dependent on the quality of the underlying maps, which, in practical industrial settings, are often procured at significant cost and may exhibit a range of inaccuracies due to survey limitations, temporal changes, and misalignments of the data. This thesis investigates the impact of map inaccuracies, specifically building rotations and translations, on localization accuracy and received signal strength Indicator(RSSI) distributions within a digital twin framework. A comprehensive methodology was developed that combined ray tracing (using Sionna), mesh manipulation (using PyVista), and Monte Carlo simulation. The process included generating randomized maps according to realistic specifications, simulating electromagnetic propagation on both baseline and perturbed maps, and systematically extracting key channel and localization metrics. Several maximum likelihood-based (ML) positioning algorithms, including Vanilla MLE, Weighted MLE, Gain-Weighted Nonlinear Least Squares and Newton-Raphson ML, were implemented and benchmarked using synthetic multipath data generated via digital twin simulations. Experimental results demonstrate that map inaccuracies can introduce significant deviations in localization, with position errors increasing as the degree of randomization increases. Although the cumulative distribution functions of the coverage map (CDFs) for path gain remain relatively robust, the evaluation shows a clear degradation in positioning accuracy with lower map fidelity. In particular, all ML-based algorithms significantly outperform baseline approaches, providing marked improvements in robustness and estimation accuracy under realistic conditions. The findings confirm that careful algorithmic selection and robust handling of ray-traced data can partially mitigate the negative effects of map imperfections. This thesis provides actionable insights for the procurement, specification, and maintenance of digital twin maps in industrial localization deployments, and highlights the necessity of integrating advanced ML-based localization algorithms to maximize reliability and operational value.