卡尔曼滤波器
α-β滤光片
扩展卡尔曼滤波器
快速卡尔曼滤波
不变扩展卡尔曼滤波器
集合卡尔曼滤波器
计算机科学
控制理论(社会学)
无味变换
控制工程
人工智能
机器学习
工程类
移动视界估计
控制(管理)
作者
Yuting Bai,Bin Yan,Chenguang Zhou,Tingli Su,Xue‐Bo Jin
标识
DOI:10.1016/j.arcontrol.2023.100909
摘要
The Kalman filter (KF) is a popular state estimation technique that is utilized in a variety of applications, including positioning and navigation, sensor networks, battery management, etc. This study presents a comprehensive review of the Kalman filter and its various enhanced models, with combining the Kalman filter with neural network methodologies. First, we provide a brief overview of the classical Kalman filter and its variants, including the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. It is pointed out that the traditional Kalman filter faces two main problems: system model and noise model parameter identification. To overcome these obstacles, researchers have developed novel solutions by integrating machine learning techniques with the Kalman filter. Secondly, this paper classifies the related models into two categories: both the internal cross-combination of the Kalman filter and neural network and their external combinations. Two different hybrid models and typical structures show that the hybrid model performs more accurately and robustly overall. Finally, the characteristic of the two hybrid models is summarized so that readers can understand them more intuitively.
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