  We introduce WiCluster, a new machine learning (ML) approach for passive
indoor positioning using radio frequency (RF) channel state information (CSI).
WiCluster can predict both a zone-level position and a precise 2D or 3D
position, without using any precise position labels during training. Prior
CSI-based indoor positioning work has relied on non-parametric approaches using
digital signal-processing (DSP) and, more recently, parametric approaches
(e.g., fully supervised ML methods). However these do not handle the complexity
of real-world environments well and do not meet requirements for large-scale
commercial deployments: the accuracy of DSP-based method deteriorates
significantly in non-line-of-sight conditions, while supervised ML methods need
large amounts of hard-to-acquire centimeter accuracy position labels. In
contrast, WiCluster is precise, requires weaker label-information that can be
easily collected, and works well in non-line-of-sight conditions. Our first
contribution is a novel dimensionality reduction method for charting. It
combines a triplet-loss with a multi-scale clustering-loss to map the
high-dimensional CSI representation to a 2D/3D latent space. Our second
contribution is two weakly supervised losses that map this latent space into a
Cartesian map, resulting in meter-accuracy position results. These losses only
require simple to acquire priors: a sketch of the floorplan, approximate
access-point locations and a few CSI packets that are labelled with the
corresponding zone in the floorplan. Thirdly, we report results and a
robustness study for 2D positioning in two single-floor office buildings and 3D
positioning in a two-story home.
