论文标题
Kalman过滤系统参数中的概率不确定性
Kalman Filtering with Probabilistic Uncertainty in System Parameters
论文作者
论文摘要
在本文中，我们为系统参数中具有概率不确定性的系统提出了一个强大的Kalman过滤框架。我们考虑两种情况，分别是离散的时间系统，以及具有离散测量的连续时间系统。不确定性以状态的平均值和方差为特征，是使用条件期望和多项式混乱扩展框架传播的。将使用拟议过滤器获得的结果与文献中现有的可靠过滤器进行了比较。提出的过滤器在根平方误差和收敛速率方面表现出更好的性能。
In this paper, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete time systems, and continuous time systems with discrete measurements. The uncertainty, characterized by mean and variance of the states, is propagated using conditional expectations and polynomial chaos expansion framework. The results obtained using the proposed filter are compared with existing robust filters in the literature. The proposed filter demonstrates better performance in terms of root mean squared error and rate of convergence.
