Notice of Violation of IEEE Publication Principles - Cholesky-based reduced-rank square-root ensemble Kalman filtering

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"Cholesky-Based Reduced-Rank Squar-Root Ensemble Kalman Filtering,"<br>by Yucheng Zhou, Jiahe Xu, Yuanwei Jing, Georgi M. Dimirovski<br>in the Proceedings of the 2010 American Control Conference, July 2010, pp. 6870-6875<br><br>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.<br><br>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.<br><br>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:<br><br>"Cholesky-Based Reduced-Rank Square-Root Kalman Filtering,"<br>by J. Chandrasekar, I.S.Kim, D.S. Bernstein, A.J. Ridley<br>in the Proceedings of the 2008 American Control Conference, June 2008, pp. 3987-3992<br><br> <br/> 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-factorizationbased reduced-order EnKF is superior to the SVD-based and offer much advantage in terms of estimation performance.