无模型容积卡尔曼滤波及其应用

提出一种融合高斯过程回归(GPR)的无模型容积卡尔曼滤波(MF-CKF)方法.容积卡尔曼滤波(CKF)是一种新的非线性高斯滤波方法,比无迹卡尔曼滤波(UKF)更具优势.为了克服建模不准确时容积卡尔曼滤波精度下降问题,通过将高斯过程回归引入到容积卡尔曼滤波之中,对训练数据学习建立系统非线性模型,从而有效地避免模型不准确造成的滤波性能下降.仿真结果验证了无模型容积卡尔曼滤波在系统模型不准确情况下的优越性.

Model-free cubature Kalman filter and its application

A model-free cubature Kalman filter(MF-CKF) combined with Gaussian process regression(GPR) is presented.
Cubature Kalman filter(CKF) is a new nonlinear Gaussian filter, which is superior than uncented Kalman filter(UKF).
Gaussian process regression is introduced into cubature Kalman filter to overcome precision decreasing caused by model
uncertainty. Gaussian process is applied to establish nonlinear models by using training data, which efficiently avoids the
degradation of filtering performance. Simulation results show the superiority of MF-CKF in the case of model uncertainty.