Passive Channel Charting: Locating Passive Targets using a UWB Mesh
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910945594310656 |
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| author | Poeggel, Raffael Stahlke, Maximilian Pirkl, Jonas Ott, Jonathan Yammine, George Feigl, Tobias Mutschler, Christopher |
| author_facet | Poeggel, Raffael Stahlke, Maximilian Pirkl, Jonas Ott, Jonathan Yammine, George Feigl, Tobias Mutschler, Christopher |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_10194 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Passive Channel Charting: Locating Passive Targets using a UWB Mesh Poeggel, Raffael Stahlke, Maximilian Pirkl, Jonas Ott, Jonathan Yammine, George Feigl, Tobias Mutschler, Christopher Signal Processing 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. |
| title | Passive Channel Charting: Locating Passive Targets using a UWB Mesh |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2505.10194 |