Sigma点卡尔曼滤波及其应用

针对扩展卡尔曼滤波(EKF)不易调整、难于应用、只对更新时间步长内局部线性假设成立的非线性系统适用等不足,近年来提出了一些卡尔曼滤波向非线性系统扩展的新方法。根据均值与协方差信息按非线性映射传播的特点,将它们归类为Sigma点卡尔曼滤波(SPKF)方法。在简要说明加权统计线性回归技术的基础上,系统介绍了SPKF的形式及算法,对其应用情况进行了总结和展望,指出可采用SPKF替代EKF以获得更好的性能。

Sigma-point Kalman filter and its application

An extended Kalman filter(EKF) is difficult to implement and tune, and only reliable for the systems that are nearly linear on the time scale of the updates, to overcome these shortages some new extension methods of Kalman filter to nonlinear systems have been proposed recently. These methods are classified as a family of filters called Sigma-point Kalman filters(SPKF). Based on brief explanation of weighted statistical linear regression technology, the form and arithmetic of SPKF are introduced, and the applications of SPKF are summarized and forcasted.