Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters
Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters
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DOI：
10.1109/lcsys.2020.3001490
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发表时间：
2021-01
影响因子：
3
通讯作者：
Sunsoo Kim;Vedang M. Deshpande;R. Bhattacharya
中科院分区：
文献类型：
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作者：
Sunsoo Kim;Vedang M. Deshpande;R. Bhattacharya
在这封信中，我们提出了一个强大的卡尔曼滤波框架，系统参数的概率不确定性。我们考虑两种情况，即离散时间系统和连续时间系统的离散测量。不确定性，其特征在于状态的均值和方差，使用条件期望和多项式混沌扩展框架传播。使用所提出的滤波器获得的结果进行比较与现有的鲁棒滤波器在文献中。该滤波器在估计误差和收敛速度方面表现出更好的性能。
In this letter, 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 estimation error and rate of convergence.