Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICASSP.2007.367062 | Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference |
Keywords | Field | DocType |
Gaussian processes,Kalman filters,Monte Carlo methods,particle filtering (numerical methods),tomography,Gaussian particle filter,Monte Carlo technique,large-scale dynamic tomography,localized ensemble Kalman filter,state estimation problem,Kalman filtering,multidimensional signal processing,recursive estimation,remote sensing | Mathematical optimization,Monte Carlo method,Extended Kalman filter,Computer science,Particle filter,Kalman filter,Dynamic Monte Carlo method,Gaussian process,Ensemble Kalman filter,Invariant extended Kalman filter | Conference |
Volume | ISSN | ISBN |
3 | 1520-6149 | 1-4244-0727-3 |
Citations | PageRank | References |
4 | 1.16 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mark D. Butala | 1 | 24 | 4.80 |
| Richard A. Frazin | 2 | 4 | 1.16 |
| Yuguo Chen | 3 | 187 | 11.67 |
| Farzad Kamalabadi | 4 | 98 | 17.82 |