@inproceedings{803c32552d084227b03e30621d63422e,
title = "A Monte Carlo technique for large-scale dynamic tomography",
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.",
keywords = "Kalman filtering, Multidimensional signal processing, Recursive estimation, Remote sensing",
author = "Butala, {Mark D.} and Frazin, {Richard A.} and Chen Yuguo and Farzad Kamalabadi",
year = "2007",
doi = "10.1109/ICASSP.2007.367062",
language = "English (US)",
isbn = "1424407281",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
pages = "III1217--III1220",
booktitle = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07",
note = "2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07 ; Conference date: 15-04-2007 Through 20-04-2007",
}