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Fingerprinting-based positioning significantly improves the indoorlocalization performance in non-line-of-sight-dominated areas. However, itsdeployment and maintenance is cost-intensive as it needs ground-truth referencesystems for both the initial training and the adaption to environmentalchanges. In contrast, channel charting (CC) works without explicit referenceinformation and only requires the spatial correlations of channel stateinformation (CSI). While CC has shown promising results in modelling thegeometry of the radio environment, a deeper insight into CC for localizationusing multi-anchor large-bandwidth measurements is still pending. We contributea novel distance metric for time-synchronized single-input/single-output CSIsthat approaches a linear correlation to the Euclidean distance. This allows tolearn the environment's global geometry without annotations. To efficientlyoptimize the global channel chart we approximate the metric with a Siameseneural network. This enables full CC-assisted fingerprinting and positioningonly using a linear transformation from the chart to the real-worldcoordinates. We compare our approach to the state-of-the-art of CC on twodifferent real-world data sets recorded with a 5G and UWB radio setup. Ourapproach outperforms others with localization accuracies of 0.69m for the UWBand 1.4m for the 5G setup. We show that CC-assisted fingerprinting enableshighly accurate localization and reduces (or eliminates) the need for annotatedtraining data.

Indoor Localization with Robust Global Channel Charting: A Time-Distance-Based Approach