Fontana, Marco 
ORCID: 0000-0003-0703-6535, Hayder, Thomas, Freilingert, William, Garcia-Fernandez, Angel F ORCID: 0000-0002-6471-8455 and Maskell, Simon ORCID: 0000-0003-1917-2913
(2024)
A Poisson Multi-Bernoulli Mixture approach to tracking trains using Distributed Acoustic Sensing
In: 2024 27th International Conference on Information Fusion (FUSION), 2024-7-8 - 2024-7-11.
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Abstract
This paper presents an extended target tracking method to track trains using Distributed Acoustic Sensing (DAS) data. The problem is approached using a measurement likelihood based on a Set of Points on a Rigid Body (SPRB) model applied to a clustered version of the Poisson Multi-Bernoulli Mixture filter. The method efficiently handles asymmetric noise within the set of measurements returned by each train, and proposes a solution to merged measurements appearing at crossings. We use experimental data obtained from trains to show that the proposed algorithm has lower localisation and false target error, leading to better performance in terms generalized optimal sub-pattern assignment (GOSPA) metric.
| Item Type: | Conference Item (Unspecified) |
|---|---|
| Uncontrolled Keywords: | Random finite sets, Bayesian estimation, extended multi-target tracking, Poisson multi-Bernoulli mixtures |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 20 Jan 2025 09:49 |
| Last Modified: | 23 May 2026 09:27 |
| DOI: | 10.23919/FUSION59988.2024.10706405 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3189771 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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