Hybrid Estimation Algorithms,Part 2 |
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Authors: | Sworder D D Boyd J E |
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Institution: | (1) University of California at San Diego, La Jolla, California;(2) Cubic Defense Systems, San Diego, California |
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Abstract: | The equations of state evolution of a hybrid system are nonlinear and generate non-Gaussian sample paths. For this reason, the optimal, mean-square estimate of the state is difficult to determine. In an earlier paper (Ref. 1), a useful approximation to the optimal estimator was derived for the case where there is a direct, albeit noisy, measurement of the modal state. Although this algorithm has proven serviceable, it is restricted to applications in which the base-state path is continuous. In this paper, the result is extended to the case in which there are base-state discontinuities of a particular sort. The algorithm is tested on a target tracking problem and is shown to be superior to both the extended Kalman filter and the estimator derived in Ref. 1. |
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Keywords: | Imaging systems filtering prediction estimation stochastic differential equations |
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