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Notice of Violation of IEEE Publication Principles
"Cholesky-Based Reduced-Rank Squar-Root Ensemble Kalman Filtering,"
by Yucheng Zhou, Jiahe Xu, Yuanwei Jing, Georgi M. Dimirovski
in the Proceedings of the 2010 American Control Conference, July 2010, pp. 6870-6875
After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.
Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:
"Cholesky-Based Reduced-Rank Square-Root Kalman Filtering,"
by J. Chandrasekar, I.S.Kim, D.S. Bernstein, A.J. Ridley
in the Proceedings of the 2008 American Control Conference, June 2008, pp. 3987-3992
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.