Hybrid estimation algorithms |
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Authors: | D. D. Sworder R. Vojak |
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Affiliation: | (1) University of California at San Diego, La Jolla, California;(2) INRIA, Domain de Voluceau, Rocqencourt, France |
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Abstract: | The optimal, mean-square estimate of the state of a hybrid system is difficult to determine because the equations of state evolution are nonlinear and non-Gaussian. When there is a direct, albeit noisy, measurement of the modal state, it is possible to derive a useful approximation to the optimal estimator. This simplified algorithm is tested on a target tracking problem, and is seen to be superior to the conventional extended Kalman filter.This research was partially supported by a grant from the Hughes Aircraft Company and by the MICRO Program of the State of California under Project No. 91-156. |
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Keywords: | Imaging systems filtering prediction estimation stochastic differential equations |
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