# A Robust State Filter Against Unmodeled Process And Measurement Noise

arXiv: https://arxiv.org/abs/2511.19157 · HTML: https://mlsanity.com/paper/2511.19157/

## 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.

## Builds on (most cited first, 2 of 2)

- [Outlier-robust Kalman Filtering through Generalised Bayes](https://mlsanity.com/paper/2405.05646/) (2024)
- [Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory](https://mlsanity.com/paper/1301.4566/) (2013)

## 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.