  Channel Charting aims to construct a map of the radio environment by
leveraging similarity relationships found in high-dimensional channel state
information. Although resulting channel charts usually accurately represent
local neighborhood relationships, even under conditions with strong multipath
propagation, they often fall short in capturing global geometric features. On
the other hand, classical model-based localization methods, such as
triangulation and multilateration, can easily localize signal sources in the
global coordinate frame. However, these methods rely heavily on the assumption
of line-of-sight channels and distributed antenna deployments. Based on
measured data, we compare classical source localization techniques to channel
charts with respect to localization performance. We suggest and evaluate
methods to enhance Channel Charting with model-based localization approaches:
One approach involves using information derived from classical localization
methods to map channel chart locations to physical positions after conventional
training of the forward charting function. Foremost, though, we suggest to
incorporate information from model-based approaches during the training of the
forward charting function in what we call "augmented Channel Charting". We
demonstrate that Channel Charting can outperform classical localization methods
on the considered dataset.
