标题： 一种更新鲁棒的卡尔曼滤波方法 显示英文标题

标题： An update-resilient Kalman filtering approach

摘要： 我们提出一种新的鲁棒滤波范式，考虑模型不确定性仅存在于观测中的情况，该不确定性通过一个模糊集进行描述。 我们推导出相应的鲁棒估计器，称为更新鲁棒卡尔曼滤波器，与现有的最小最大博弈基础滤波方法相比，它似乎是新颖的。 此外，我们表征了相应的最不利状态空间模型，并分析了滤波器的稳定性。 最后，一些数值示例展示了所提出的估计器的有效性。 显示英文摘要

摘要： We propose a new robust filtering paradigm considering the situation in which model uncertainty, described through an ambiguity set, is present only in the observations. We derive the corresponding robust estimator, referred to as update-resilient Kalman filter, which appears to be novel compared to existing minimax game-based filtering approaches. Moreover, we characterize the corresponding least favorable state space model and analyze the filter stability. Finally, some numerical examples show the effectiveness of the proposed estimator.