Cholesky-based reduced-rank square-root ensemble Kalman filtering
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KünyeZhou, Y., Xu, J., Jing, Y., & Dimirovski, G. M. (2010). Cholesky-based reduced-rank square-root ensemble Kalman filtering. In 2010 American Control Conference (ACC) (pp. 6870-6875). Piscataway, NJ: IEEE. http://dx.doi.org/10.1109/ACC.2010.5531566
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.