A Robust State Filter Against Unmodeled Process And Measurement Noise
arXiv:2511.19157v1
Originality Incremental advance
AI Analysis
This addresses state estimation problems in systems with unmodeled noise, but appears incremental as it builds on existing methods like WoLF.
The paper tackles robust state estimation under process and measurement noise by introducing a novel Kalman filter framework based on a generalized Bayesian approach, achieving improved robustness against outliers.
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.