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An update-resilient Kalman filtering approach
[Submitted on 10 Apr 2025 (v1), last revised 22 May 2026 (this v · 2026-05-25 · via math updates on arXiv.org

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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.

Submission history

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)