{"ID":2890537,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19327","arxiv_id":"2507.19327","title":"Real-time rail vehicle localisation using spatially resolved magnetic field measurements","abstract":"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.","short_abstract":"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 seque...","url_abs":"https://arxiv.org/abs/2507.19327","url_pdf":"https://arxiv.org/pdf/2507.19327v1","authors":"[\"Niklas Dieckow\",\"Katharina Ostaszewski\",\"Philip Heinisch\",\"Henriette Struckmann\",\"Hendrik Ranocha\"]","published":"2025-07-25T14:38:16Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"eess.SY\"]","methods":"[]","has_code":false}