A Monte Carlo Technique for Large-Scale Dynamic Tomography
Butala, M.D.
Frazin, R.A.
Yuguo Chen
Kamalabadi, F.
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL;
This paper appears in: Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on
Publication Date: 15-20 April 2007
Volume: 3,
On page(s): III-1217-III-1220
Location: Honolulu, HI,
ISSN: 1520-6149
ISBN: 1-4244-0727-3
INSPEC Accession Number: 9497483
Digital Object Identifier: 10.1109/ICASSP.2007.367062
Current Version Published: 2007-06-04
Abstract
We address the reconstruction of a physically evolving unknown from tomographic measurements by formulating it as a state estimation problem. The approach presented in this paper is the localized ensemble Kalman filter (LEnKF); a Monte Carlo state estimation procedure that is computationally tractable when the state dimension is large. We establish the conditions under which the LEnKF is equivalent to the Gaussian particle filter. The performance of the LEnKF is evaluated in a numerical example and is shown to give state estimates of almost equal quality as the optimal Kalman filter but at a 95% reduction in computation
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