A Robust State Filter Against Unmodeled Process And Measurement Noise
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914169253527552 |
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| author | Liu, Weitao |
| author_facet | Liu, Weitao |
| contents | This paper introduces a novel Kalman filter framework designed to achieve robust state estimation under both process and measurement noise. Inspired by the Weighted Observation Likelihood Filter (WoLF), which provides robustness against measurement outliers, we applied generalized Bayesian approach to build a framework considering both process and measurement noise outliers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19157 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Robust State Filter Against Unmodeled Process And Measurement Noise Liu, Weitao Machine Learning This paper introduces a novel Kalman filter framework designed to achieve robust state estimation under both process and measurement noise. Inspired by the Weighted Observation Likelihood Filter (WoLF), which provides robustness against measurement outliers, we applied generalized Bayesian approach to build a framework considering both process and measurement noise outliers. |
| title | A Robust State Filter Against Unmodeled Process And Measurement Noise |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.19157 |