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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2014/42495
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| Title: | A localized ensemble Kalman smoother |
| Authors: | Butala, Mark D. |
| Keywords: | multidimensional signal processing
recursive estimation
Kalman filter
remote sensing |
| Issue Date: | 5-Aug-2012 |
| Publisher: | Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2012. |
| Citation: | IEEE - 2012 IEEE Statistical Signal Processing Workshop (SSP), Ann Arbor, Michigan, August 5-8, 2012 |
| Abstract: | Numerous geophysical inverse problems prove difficult because the available measurements are indirectly related to the underlying unknown dynamic state and the physics governing the system may involve imperfect models or unobserved parameters. Data assimilation addresses these difficulties by combining the measurements and physical knowledge. The main challenge in such problems usually involves their high dimensionality and the standard statistical methods prove computationally intractable. This paper develops and addresses the theoretical convergence of a new high-dimensional Monte-Carlo approach called the localized ensemble Kalman smoother. |
| URI: | http://hdl.handle.net/2014/42495 |
| Appears in Collections: | JPL TRS 1992+
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