UKF滤波算法在数据同化中的应用

黄春林; 李新

UKF滤波算法在数据同化中的应用

黄春林,
李新

中国科学院寒区旱区环境与工程研究所, 甘肃, 兰州, 730000

基金项目: 国家自然科学基金项目(90202014);国家重点基础研究发展项目(2001CB309404);中国科学院寒区旱区环境与工程研究所创新课题(CACX2003102);中国科学院西部行动计划(二期)项目(KZCX2-XB2-09)共同资助

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出版历程
- 收稿日期: 2006-09-04
- 修回日期: 2007-03-28

APPLICATION OF UNSCENTED KALMAN FILTER FOR DATA ASSIMILATION

Cold and Arid Regions Environmental and Engineering Research Institute, CAS, Lanzhou 730000, China

摘要

摘要: 介绍了一种新的数据同化算法(UKF,Unscented Kalman Filter),该算法不需要计算伴随矩阵,就能够解决模式的非线性问题。以Lorenz系统为例,进行了数据同化的数值试验。结果表明:基于UKF的同化方案与背景场的初始值无关,它能有效地抑制状态变量误差的增长,同化结果精度高。
- UKF滤波 /
- 数据同化 /
- Lorenz模型
Abstract: Unscented Kalman Filter(UKF) is a new data assimilation algorithm,which does not need adjoint matrix and can resolve the problem of nonlinearity existing in models.In this paper,the UKF algorithm is described in detail.The Lorenz model is used in numerical experiments to examine the performance of UKF.The results show that the data assimilation scheme based on UKF is independent of the first guess of background field.This method can also restrain the increase of state error and the assimilation results are satisfying.
- Unscented Kalman filter /
- data assimilation /
- Lorenz model

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参考文献(17)

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