平滑的
卡尔曼滤波器
贝叶斯概率
非线性系统
数学优化
算法
计算机科学
无味变换
扩展卡尔曼滤波器
状态空间
控制理论(社会学)
数学
应用数学
人工智能
统计
集合卡尔曼滤波器
计算机视觉
物理
量子力学
控制(管理)
出处
期刊:IEEE Transactions on Automatic Control
[Institute of Electrical and Electronics Engineers]
日期:2008-04-08
卷期号:53 (3): 845-849
被引量:248
标识
DOI:10.1109/tac.2008.919531
摘要
This note considers the application of the unscented transform to optimal smoothing of nonlinear state--space models. In this note, a new Rauch--Tung--Striebel type form of the fixed-interval unscented Kalman smoother is derived. The new smoother differs from the previously proposed two-filter-formulation-based unscented Kalman smoother in the sense that it is not based on running two independent filters forward and backward in time. Instead, a separate backward smoothing pass is used, which recursively computes corrections to the forward filtering result. The smoother equations are derived as approximations to the formal Bayesian optimal smoothing equations. The performance of the new smoother is demonstrated with a simulation.
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