Hybrid Estimation Algorithms, Part 2

D. D. Sworder and J. E. Boyd
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D. D. Sworder: University of California at San Diego
J. E. Boyd: Cubic Defense Systems

Journal of Optimization Theory and Applications, 1997, vol. 94, issue 1, No 1, 21 pages

Abstract: 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.

Keywords: Imaging systems; filtering; prediction; estimation; stochastic differential equations (search for similar items in EconPapers)
Date: 1997
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DOI: 10.1023/A:1022618500543

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