Minimax Performance Limits for Multiple-Model Estimation
Fuente:
arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866916181426831360 |
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| author | Kjellqvist, Olle |
| author_facet | Kjellqvist, Olle |
| contents | This article concerns the performance limits of strictly causal state estimation for linear systems with fixed, but uncertain, parameters belonging to a finite set. In particular, we provide upper and lower bounds on the smallest achievable gain from disturbances to the point-wise estimation error. The bounds rely on forward and backward Riccati recursions -- one forward recursion for each feasible model and one backward recursion for each pair of feasible models. We give simple examples where the lower and upper bounds are tight. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_05159 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Minimax Performance Limits for Multiple-Model Estimation Kjellqvist, Olle Optimization and Control This article concerns the performance limits of strictly causal state estimation for linear systems with fixed, but uncertain, parameters belonging to a finite set. In particular, we provide upper and lower bounds on the smallest achievable gain from disturbances to the point-wise estimation error. The bounds rely on forward and backward Riccati recursions -- one forward recursion for each feasible model and one backward recursion for each pair of feasible models. We give simple examples where the lower and upper bounds are tight. |
| title | Minimax Performance Limits for Multiple-Model Estimation |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2312.05159 |