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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2506.04815 |
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| _version_ | 1866912415107514368 |
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| author | Yi, Shenglun Zorzi, Mattia |
| author_facet | Yi, Shenglun Zorzi, Mattia |
| contents | In this paper, we address a robust nonlinear state estimation problem under model uncertainty by formulating a dynamic minimax game: one player designs the robust estimator, while the other selects the least favorable model from an ambiguity set of possible models centered around the nominal one. To characterize a closed-form expression for the conditional expectation characterizing the estimator, we approximate the center of this ambiguity set by means of a sigma point approximation. Furthermore, since the least favorable model is generally nonlinear and non-Gaussian, we derive a simulator based on a Markov chain Monte Carlo method to generate data from such model. Finally, some numerical examples show that the proposed filter outperforms the existing filters. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_04815 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A robust approach to sigma point Kalman filtering Yi, Shenglun Zorzi, Mattia Optimization and Control In this paper, we address a robust nonlinear state estimation problem under model uncertainty by formulating a dynamic minimax game: one player designs the robust estimator, while the other selects the least favorable model from an ambiguity set of possible models centered around the nominal one. To characterize a closed-form expression for the conditional expectation characterizing the estimator, we approximate the center of this ambiguity set by means of a sigma point approximation. Furthermore, since the least favorable model is generally nonlinear and non-Gaussian, we derive a simulator based on a Markov chain Monte Carlo method to generate data from such model. Finally, some numerical examples show that the proposed filter outperforms the existing filters. |
| title | A robust approach to sigma point Kalman filtering |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2506.04815 |