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A Robust State Filter Against Unmodeled Process And Measurement Noise

arXiv 2511.19157 ↗First posted 24 Nov 2025stat.ML cs.LGcited by 0

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Abstract (arXiv)

This paper introduces a novel Kalman filter framework designed to achieve robust state estimation under both process and measurement noise. Inspired by the Weighted Observation Likelihood Filter (WoLF), which provides robustness against measurement outliers, we applied generalized Bayesian approach to build a framework considering both process and measurement noise outliers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 24 references that are in the map. First posted on arXiv 24 Nov 2025.

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References · 2

  1. 2024Outlier-robust Kalman Filtering through Generalised Bayescited by 21
  2. 2013Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theorycited by 3

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