文献类型：会议论文

英文题名：Cholesky-Based Reduced-Rank Square-Root Ensemble Kalman Filtering

作者：Zhou, Yucheng[1] Xu, Jiahe[1] Jing, Yuanwei[2] Dimirovski, Georgi M.[3,4]

第一作者：周玉成

通信作者：Zhou, YC[1]

机构：[1]Chinese Acad Forestry, Inst Wood Ind, Dept Res, Fac Engn, Beijing 100091, Peoples R China;[2]Northeastern Univ, Fac Informat Sci & Engn, Shenyang AH-110004, Liaoning, Peoples R China;[3]Saints Cyril & Methodius Univ Skopje, Fac FEIT, MK-1000 Skopje, North Macedonia;[4]Dogus Univ, Fac Engn, TR-34722 Istanbul, Turkey

会议论文集：American Control Conference

会议日期：JUN 30-JUL 02, 2010

会议地点：Baltimore, MD

语种：英文

外文关键词：Large scale systems - Monte Carlo methods - Singular value decomposition

年份：2010

摘要：The reduced-order ensemble Kalman filter (EnKF) is introduced to the problem of state estimation for nonlinear large-scale systems. The filter reduction based on both the singular value decomposition (SVD) and the Cholesky decomposition provide for reduced-order square-root EnKF. To solve the filter reduction, the EnKF algorithm is modified to obtain members of measurement ensemble from uncorrelated sensors in the system but not a Monte Carlo method, and the performances of the reduced-order EnKF under different conditions are investigated. Simulation shows that the Cholesky-factorization-based reduced-order EnKF is superior to the SVD-based and offer much advantage in terms of estimation performance.