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Hybrid Estimation Algorithms, Part 2

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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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Authors and Affiliations

  1. University of California at San Diego, La Jolla, California

    D. D. Sworder (Acting Vice Chancellor for Research)

  2. Cubic Defense Systems, San Diego, California

    J. E. Boyd (Principal Staff Scientist)

Authors
  1. D. D. Sworder
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  2. J. E. Boyd
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Sworder, D.D., Boyd, J.E. Hybrid Estimation Algorithms, Part 2. Journal of Optimization Theory and Applications 94, 1–21 (1997). https://doi.org/10.1023/A:1022618500543

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