🤖 AI Summary
Traditional Kalman filtering lacks robustness against systems subject to unmodeled process and measurement noise. To address this, we propose a novel generalized Bayesian robust filtering framework that, for the first time, extends the weighted observation likelihood mechanism to the process noise modeling component, enabling joint suppression of process and measurement outliers. Our method constructs a dual-weighted likelihood function to uniformly characterize non-Gaussian anomalies in both process dynamics and observations, and performs robust recursive state estimation within a generalized Bayesian inference framework. Experimental results demonstrate that, under significant process or measurement outliers, the proposed approach achieves substantially higher estimation accuracy and stability compared to the standard Kalman filter and existing robust filters. These findings validate both the theoretical soundness and practical applicability of the method.
📝 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.