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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2504.07847 |
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| _version_ | 1866913155923312640 |
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| author | Yi, Shenglun Zorzi, Mattia |
| author_facet | Yi, Shenglun Zorzi, Mattia |
| contents | We propose a new robust filtering paradigm considering the situation in which model uncertainty, described through an ambiguity set, is present only in the observations. We derive the corresponding robust estimator, referred to as update-resilient Kalman filter, which appears to be novel compared to existing minimax game-based filtering approaches. Moreover, we characterize the corresponding least favorable state space model and analyze the filter stability. Finally, some numerical examples show the effectiveness of the proposed estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07847 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An update-resilient Kalman filtering approach Yi, Shenglun Zorzi, Mattia Optimization and Control We propose a new robust filtering paradigm considering the situation in which model uncertainty, described through an ambiguity set, is present only in the observations. We derive the corresponding robust estimator, referred to as update-resilient Kalman filter, which appears to be novel compared to existing minimax game-based filtering approaches. Moreover, we characterize the corresponding least favorable state space model and analyze the filter stability. Finally, some numerical examples show the effectiveness of the proposed estimator. |
| title | An update-resilient Kalman filtering approach |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2504.07847 |