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Stable Robust Extended Kalman Filter

机译：稳定的鲁棒扩展卡尔曼滤波器

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In this paper, a stable and robust filter is proposed for structural identification. This filter resolves the instability problems of the traditional extended Kalman filter (EKF). Instead of ad hoc assignment of the noise covariance matrices in the EKF, the proposed stable robust extended Kalman filter (SREKF) provides real-time updating of the noise parameters. This resolves the well-known instability problem of the EKF due to improper assignment of the noise covariance matrices. Furthermore, the proposed SREKF is capable of removing abnormal data points in a real-time manner. As a result, the parametric identification results will be more reliable and have fewer fluctuations. The proposed approach will be applied to structural damage detection of degrading linear and nonlinear structures in comparison with the plain EKF, utilizing highly contaminated response measurements. It turns out that the estimation error of the state vector and the structural parameters is lower than the EKF by one and two orders of magnitude, respectively. (C) 2016 American Society of Civil Engineers.
机译：本文提出了一种稳定，鲁棒的滤波器用于结构识别。该滤波器解决了传统扩展卡尔曼滤波器（EKF）的不稳定性问题。代替在EKF中临时分配噪声协方差矩阵，建议的稳定鲁棒扩展卡尔曼滤波器（SREKF）提供了噪声参数的实时更新。这解决了由于噪声协方差矩阵分配不当而导致的EKF不稳定问题。此外，提出的SREKF能够实时删除异常数据点。结果，参数识别结果将更加可靠并且波动较小。与平原EKF相比，该方法将利用高度污染的响应测量结果，应用于退化线性和非线性结构的结构损伤检测。结果表明，状态向量和结构参数的估计误差分别比EKF低一个和两个数量级。 （C）2016年美国土木工程师学会。

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