K. Ide, M. Ghil, Extended Kalman filtering for vortex systems. Part II: Rankine vortices and observing-system design, Dyn. of Atmos. and Oceans 27 (1-4) (1998) 333--350. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Graphical Models for Statistical Inference and Data.. - Ihler, Kirshner.. (2005) Self-citation (Ghil) (Correct)
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K. Ide, M. Ghil, Extended Kalman filtering for vortex systems. Part II: Rankine vortices and observing-system design, Dyn. of Atmos. and Oceans 27 (1-4) (1998) 333--350.
Graphical Models for Statistical Inference and Data.. - Ihler, Kirshner.. (2005) Self-citation (Ghil) (Correct)
No context found.
K. Ide, M. Ghil, Extended Kalman filtering for vortex systems. Part I: Methodology and point vortices, Dyn. of Atmos. and Oceans 27 (1-4) (1998) 301--332. 27
Unified Notation for Data Assimilation: Operational.. - Ide, Courtier, al. (1997) (3 citations) Self-citation (Ide) (Correct)
....still denoted by P and R, for simplicity. Since the observations y o do occur, typically, at discrete times, the EKF forecast step between two arbitrarily spaced observation times is x f = M(x f ; t) A:4a) P f = M f P f (P f ) T M f Q; A:4b) with M(t) M 0 (t) e.g. Ide and Ghil, 1996a,b; Jazwinski, 1970) The linearization in the EKF itself is about x f (t) and various suboptimal filters with linearization about x b (t) may be considered. Since it is unlikely that both time continuous and time discrete versions of the EKF will be used in the same paper, it seems ....
Ide, K., and M. Ghil, 1996: Extended Kalman filtering for vortex systems. Part II: Rankine vortices and observing-system design. Dyn. Atmos. Oceans, accepted.
Unified Notation for Data Assimilation: Operational.. - Ide, Courtier, al. (1997) (3 citations) Self-citation (Ide) (Correct)
....still denoted by P and R, for simplicity. Since the observations y o do occur, typically, at discrete times, the EKF forecast step between two arbitrarily spaced observation times is x f = M(x f ; t) A:4a) P f = M f P f (P f ) T M f Q; A:4b) with M(t) M 0 (t) e.g. Ide and Ghil, 1996a,b; Jazwinski, 1970) The linearization in the EKF itself is about x f (t) and various suboptimal filters with linearization about x b (t) may be considered. Since it is unlikely that both time continuous and time discrete versions of the EKF will be used in the same paper, it seems ....
Ide, K., and M. Ghil, 1996: Extended Kalman filtering for vortex systems. Part I: Methodology and point vortices. Dyn. Atmos. Oceans, accepted.
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