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J. T. Ambadan and Y. Tang, “Sigma-Point Kalman Filter Data Assimilation Methods for Strongly Nonlinear Systems,” Journal of the Atmospheric Sciences, Vol. 66, No. 2, 2009, pp. 261-285. doi:10.1175/2008JAS2681.1

has been cited by the following article:

• TITLE: Adaptive Filter for High Dimensional Inverse Engineering Problems: From Theory to Practical Implementation

  AUTHORS: Hong Son Hoang, Rémy Barailles

  KEYWORDS: Dynamical Systems; Adaptive Filtering; Stability; Real Schur Decomposition; Stochastic Approximation; Numerical Prediction; Data Assimilation

  JOURNAL NAME: Engineering, Vol.5 No.5A, May 24, 2013

  ABSTRACT: The inverse engineering problems approach is a discipline that is growing very rapidly. The inverse problems we consider here concern the way to determine the state and/or parameters of the physical system of interest using observed measurements. In this context the filtering algorithms constitute a key tool to offer improvements of our knowledge on the system state, its forecast… which are essential, in particular, for oceanographic and meteorologic operational systems. The objective of this paper is to give an overview on how one can design a simple, no time-consuming Reduced-Order Adaptive Filter (ROAF) to solve the inverse engineering problems with high forecasting performance in very high dimensional environment.