By Topic

Fast implementations of the Kalman-Bucy filter for satellite data assimilation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
A. Asif ; Dept. of Comput. Sci., York Univ., Toronto, Ont., Canada

We present practical data assimilation algorithms based on the Kalman-Bucy filter (KBf) for combining satellite altimetry data with the nonlinear ocean circulation models. Data assimilation in such applications is computationally challenging because of the large dimensions of the state fields. Compared with the direct KBf, our KBf implementations provide computational savings of two orders of the magnitude of the linear dimension of the state field. We run twin experiments by interfacing our data assimilation algorithms with the NLOM, a nonlinear ocean circulation model developed at the Naval Research Laboratory.

Published in:

IEEE Signal Processing Letters  (Volume:11 ,  Issue: 2 )