Abstract: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.
| Comments: | Accepted to Automatica |
| Subjects: | Optimization and Control (math.OC) |
| Cite as: | arXiv:2504.07847 [math.OC] |
| (or arXiv:2504.07847v2 [math.OC] for this version) | |
| https://doi.org/10.48550/arXiv.2504.07847 arXiv-issued DOI via DataCite |
From: Mattia Zorzi [view email]
[v1]
Thu, 10 Apr 2025 15:26:48 UTC (246 KB)
[v2]
Fri, 22 May 2026 14:59:53 UTC (176 KB)
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