Sensing using radio frequency (RF) signals such as Wi-Fi has garnered significant attention in recent years. They can be used, for instance, for so-called passive indoor positioning of humans. This passive positioning uses the Wi-Fi signal as a bi-static radar to determine the location of a human subject who is not carrying any Wi-Fi device. While previous works have demonstrated that positioning is possible, these algorithms rely on precise position labels for training, and only work in confined laboratory environments that must remain invariant.
We recently proposed two novel algorithms for passive positioning. The first is based on a self-supervision signal by a combined clustering and triplet loss. The second is modality-agnostic and is based on a low-dimensional manifold learning facilitated by optimal transport. Neither algorithm requires dense labels as required by state of the art algorithms. In this demo, we demonstrate results of these two algorithms in real-world environments, i.e., outside of carefully controlled labs. The presented results demonstrate that our methods surpass state of the art by a wide margin.