| Paper: | SPTM-P5.1 | ||
| Session: | Adaptive Systems and Signal Processing | ||
| Time: | Wednesday, May 19, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Detection, Estimation, and Class. Thry & Apps. | ||
| Title: | STATE ESTIMATION FROM HIGH-DIMENSIONAL DATA | ||
| Authors: | Victor Solo; University of New South Wales | ||
| Abstract: | It is implicit in traditional discussions of linear ornonlinear state estimation filters that there is no relationspecified between the dimension of the state and the observationvector dimension. If anything though, the state would often be thoughtto have higher dimension. But increasingly in practiceproblems are arising where the reverse is the case.In this paper we show that state estimation filters,such as the Kalman filter undergo a remarkable simplificationin structure and computation when the observation dimensionis much larger than the state dimension. Both linear and nonlinearcases (including point processes) are discussed. | ||
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