非线性系统的自适应推广的kalman滤波
Adaptive Extended Kalman Filtering for Nonlinear Systems
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摘要: 本文提出了未知噪声统计的非线性系统中新的自适应推广的Kalman滤波算法.作者提 出了用虚拟时变噪声统计[1,2],补偿线性化模型误差的新思想. 在本文中,作者指出了文献[3]中,用Sage和Husa的常值噪声统计估值器来估计虚拟 噪声是不合理的.另外,即使原非线性系统的噪声统计是零均值,但线性化的模型的噪声统 计一般是非零均值的.两个数值模拟例子说明了本文方法的有效性.Abstract: A new adaptive extended Kahnan filtering algorithm is given for nonlinear systems with unknown noise statistics. A new approach of compensating linearized model errors by the fictitious time-varying noise statistics[1,2] is proposed. In this paper, the authors point out that it is not suitable in reference [2] to estimate the fictitious noise by using Sage and Husa's constant noise estimators. In addition, though ,noises of the linearized systems are not generally zeromean noises. Two simulation examples are given to show the usefulness of the authors' approach.
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