Cholesky-based reduced-rank square-root ensemble Kalman filtering

Dimirovski, Georgi M. (Dogus Author) -- Conference full title: 2010 American Control Conference, ACC 2010; Baltimore, MD; United States; 30 June 2010 through 2 July 2010The 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-factorizationbased reduced-order EnKF is superior to the SVD-based and offer much advantage in terms of estimation performance.AIAA, AIChE, AIST, ASCE, IEEE -- This work is supported by the Forestry Projects in the National Science & Technology Pillar Program 2006BAD18130805 and Key Research Projects for the Forestry Science & Technology 2006-60, P. R. of China