Real-time rail vehicle localisation using spatially resolved magnetic field measurements
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912501209235456 |
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| author | Dieckow, Niklas Ostaszewski, Katharina Heinisch, Philip Struckmann, Henriette Ranocha, Hendrik |
| author_facet | Dieckow, Niklas Ostaszewski, Katharina Heinisch, Philip Struckmann, Henriette Ranocha, Hendrik |
| contents | This work presents two complementary real-time rail vehicle localization methods based on magnetic field measurements and a pre-recorded magnetic map. The first uses a particle filter reweighted via magnetic similarity, employing a heavy-tailed non-Gaussian kernel for enhanced stability. The second is a stateless sequence alignment technique that transforms real-time magnetic signals into the spatial domain and matches them to the map using a similarity measure. Experiments with operational train data show that the particle filter achieves track-selective, sub-5-meter accuracy over 21.6 km, though its performance degrades at low speeds and during cold starts. Accuracy tests were constrained by the GNSS-based reference system. In contrast, the alignment-based method excels in cold-start scenarios, localizing within 30 m in 92 % of tests (100 % using top-3 matches). A hybrid approach combines both methods$\unicode{x2014}$alignment-based initialization followed by particle filter tracking. Runtime analysis confirms real-time capability on consumer-grade hardware. The system delivers accurate, robust localization suitable for safety-critical rail applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_19327 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-time rail vehicle localisation using spatially resolved magnetic field measurements Dieckow, Niklas Ostaszewski, Katharina Heinisch, Philip Struckmann, Henriette Ranocha, Hendrik Signal Processing Systems and Control This work presents two complementary real-time rail vehicle localization methods based on magnetic field measurements and a pre-recorded magnetic map. The first uses a particle filter reweighted via magnetic similarity, employing a heavy-tailed non-Gaussian kernel for enhanced stability. The second is a stateless sequence alignment technique that transforms real-time magnetic signals into the spatial domain and matches them to the map using a similarity measure. Experiments with operational train data show that the particle filter achieves track-selective, sub-5-meter accuracy over 21.6 km, though its performance degrades at low speeds and during cold starts. Accuracy tests were constrained by the GNSS-based reference system. In contrast, the alignment-based method excels in cold-start scenarios, localizing within 30 m in 92 % of tests (100 % using top-3 matches). A hybrid approach combines both methods$\unicode{x2014}$alignment-based initialization followed by particle filter tracking. Runtime analysis confirms real-time capability on consumer-grade hardware. The system delivers accurate, robust localization suitable for safety-critical rail applications. |
| title | Real-time rail vehicle localisation using spatially resolved magnetic field measurements |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2507.19327 |