A Robust State Filter Against Unmodeled Process And Measurement Noise

TL;DR

This paper presents a new robust Kalman filter framework that effectively estimates system states despite unmodeled process and measurement noise, enhancing robustness against outliers.

Contribution

It introduces a generalized Bayesian approach to develop a Kalman filter that handles both process and measurement noise outliers, inspired by WoLF.

Findings

Improved robustness against measurement outliers.

Effective handling of unmodeled process noise.

Enhanced state estimation accuracy.

Abstract

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.