Tomographic Imaging of Dynamic Objects With the Ensemble Kalman Filter
Butala, M.D.
Frazin, R.A.
Chen, Y.
Kamalabadi, F.
Remote Sensing & Space Sci. Group, Univ. of Illinois at Urbana-Champaign, Urbana, IL;
This paper appears in: Image Processing, IEEE Transactions on
Publication Date: July 2009
Volume: 18,
Issue: 7
On page(s): 1573-1587
ISSN: 1057-7149
INSPEC Accession Number: 10704728
Digital Object Identifier: 10.1109/TIP.2009.2017996
First Published: 2009-05-12
Current Version Published: 2009-06-12
Abstract
We address the image formation of a dynamic object from projections by formulating it as a state estimation problem. The problem is solved with the ensemble Kalman filter (EnKF), a Monte Carlo algorithm that is computationally tractable when the state dimension is large. In this paper, we first rigorously address the convergence of the EnKF. Then, the effectiveness of the EnKF is demonstrated in a numerical experiment where a highly variable object is reconstructed from its projections, an imaging modality not yet explored with the EnKF. The results show that the EnKF can yield estimates of almost equal quality as the optimal Kalman filter but at a fraction of the computational effort. Further experiments explore the rate of convergence of the EnKF, its performance relative to an idealized particle filter, and implications of modeling the system dynamics as a random walk.
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
