期刊
SIGNAL PROCESSING
卷 96, 期 -, 页码 290-299出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sigpro.2013.08.015
关键词
Cubature Kalman filter; Smooth variable structure filter; Sliding mode estimation; Nonlinear estimation; Robust filtering
In this paper, nonlinear state estimation problems with modeling uncertainties are considered. As demonstrated recently in literature, the cubature Kalman filter (CKF) provides the closest known approximation to the Bayesian filter in the sense of preserving second-order information contained in noisy measurements under the Gaussian assumption. The smooth variable structure filter (SVSF) has also been recently introduced and has been shown to be robust to modeling uncertainties. In an effort to utilize the accuracy of the CKF and the robustness of the SVSF, the CKF and SVSF have been combined resulting in an algorithm referred to as the CK-SVSF. The robustness and accuracy of the CK-SVSF was validated by testing it on two different computer problems, namely, a target tracking problem and the estimation of the effective bulk modulus in an electrohydrostatic actuator. (C) 2013 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据