Passive Channel Charting: Locating Passive Targets using a UWB Mesh

TL;DR

This paper introduces a novel passive localization method using channel charting with UWB sensors, achieving high accuracy without extensive fingerprint data collection by leveraging a distance-preserving embedding derived from pedestrian dead reckoning.

Contribution

It extends channel charting to passive targets in UWB networks, combining PDR with manifold learning for accurate localization without active signal emission.

Findings

Achieves an average localization error of 0.24 meters.

Works effectively across different targets with minimal training data.

Demonstrates high accuracy in a fully connected UWB mesh network.

Abstract

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 \ac{csi} onto a 2D coordinate space. So far, researchers 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.