Passive Channel Charting: Locating Passive Targets using a UWB Mesh

Fingerprint-based passive localization enables high localization accuracy using low-cost UWB IoT radio sensors. However, fingerprinting demands extensive effort for data acquisition. The concept of channel charting reduces this effort by modeling and projecting the manifold of channel state information (CSI) onto a 2D coordinate space. So far, researchers have only applied this concept to active radio localization, where a mobile device intentionally and actively emits a specific signal.In this paper, we apply channel charting to passive localization. We use a pedestrian dead reckoning (PDR) system to estimate a target's velocity and derive a distance matrix from it. We then use this matrix to learn a distance-preserving embedding in 2D space, which serves as a fingerprinting model. In our experiments, we deploy six nodes in a fully connected ultra-wideband (UWB) mesh network to show that our method achieves high localization accuracy, with an average error of just 0.24 m, even when we train and test on different targets.