扩展卡尔曼滤波器
不变扩展卡尔曼滤波器
计算机科学
控制理论(社会学)
卡尔曼滤波器
估计员
稳健性(进化)
协方差
线性化
协方差交集
线性系统
非线性滤波器
非线性系统
滤波器(信号处理)
数学
滤波器设计
人工智能
计算机视觉
统计
数学分析
物理
基因
量子力学
化学
生物化学
控制(管理)
作者
Simon Julier,Jeffrey Uhlmann
摘要
The Kalman Filter (KF) is one of the most widely used methods for tracking and estimation due to its simplicity, optimality, tractability and robustness. However, the application of the KF to nonlinear systems can be difficult. The most common approach is to use the Extended Kalman Filter (EKF) which simply linearizes all nonlinear models so that the traditional linear Kalman filter can be applied. Although the EKF (in its many forms) is a widely used filtering strategy, over thirty years of experience with it has led to a general consensus within the tracking and control community that it is difficult to implement, difficult to tune, and only reliable for systems which are almost linear on the time scale of the update intervals. In this paper a new linear estimator is developed and demonstrated. Using the principle that a set of discretely sampled points can be used to parameterize mean and covariance, the estimator yields performance equivalent to the KF for linear systems yet generalizes elegantly to nonlinear systems without the linearization steps required by the EKF. We show analytically that the expected performance of the new approach is superior to that of the EKF and, in fact, is directly comparable to that of the second order Gauss filter. The method is not restricted to assuming that the distributions of noise sources are Gaussian. We argue that the ease of implementation and more accurate estimation features of the new filter recommend its use over the EKF in virtually all applications.
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