控制与决策
控製與決策
공제여결책
CONTROL AND DECISION
2013年
5期
769-773
,共5页
非线性滤波%容积卡尔曼滤波%高斯过程回归%模型不确定性
非線性濾波%容積卡爾曼濾波%高斯過程迴歸%模型不確定性
비선성려파%용적잡이만려파%고사과정회귀%모형불학정성
nonlinear filtering%cubature Kalman filter%Gaussian process regression%model uncertainty
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.
提齣一種融閤高斯過程迴歸(GPR)的無模型容積卡爾曼濾波(MF-CKF)方法.容積卡爾曼濾波(CKF)是一種新的非線性高斯濾波方法,比無跡卡爾曼濾波(UKF)更具優勢.為瞭剋服建模不準確時容積卡爾曼濾波精度下降問題,通過將高斯過程迴歸引入到容積卡爾曼濾波之中,對訓練數據學習建立繫統非線性模型,從而有效地避免模型不準確造成的濾波性能下降.倣真結果驗證瞭無模型容積卡爾曼濾波在繫統模型不準確情況下的優越性.
제출일충융합고사과정회귀(GPR)적무모형용적잡이만려파(MF-CKF)방법.용적잡이만려파(CKF)시일충신적비선성고사려파방법,비무적잡이만려파(UKF)경구우세.위료극복건모불준학시용적잡이만려파정도하강문제,통과장고사과정회귀인입도용적잡이만려파지중,대훈련수거학습건립계통비선성모형,종이유효지피면모형불준학조성적려파성능하강.방진결과험증료무모형용적잡이만려파재계통모형불준학정황하적우월성.
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.
