Unscented filtering and nonlinear estimation
Unscented filtering and nonlinear estimation
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DOI：
10.1109/jproc.2003.823141
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发表时间：
2004-03-01
影响因子：
20.6
通讯作者：
Uhlmann, JK
中科院分区：
文献类型：
--
作者：
Julier, SJ;Uhlmann, JK
扩展的卡尔曼滤波器（EKF）可能是非线性系统使用最广泛的估计算法。但是，在估算社区中有超过35年的经验表明，难以实施，难以调整，并且仅对在更新时间尺度上几乎是线性的系统可靠。这些困难中有许多是由于其使用线性化而引起的。为了克服这一局限性，开发了无忧的转换（UT），作为通过非线性转换传播平均值和协方差信息的一种方法。它更准确，更易于实现，并使用与线性化相同的计算顺序。本文回顾了UT的动机，开发，使用和影响。
The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.