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.

翻译：本文提出了一种新颖的卡尔曼滤波器框架，旨在实现过程噪声和测量噪声共同作用下的鲁棒状态估计。受加权观测似然滤波器（WoLF）的启发——该滤波器能有效抵抗测量异常值的影响，我们应用广义贝叶斯方法构建了一个同时考虑过程噪声与测量噪声异常值的框架。