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Hybrid Estimation Algorithms, Part 2

Author

Listed:
  • D. D. Sworder

    (University of California at San Diego)

  • J. E. Boyd

    (Cubic Defense Systems)

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.

Suggested Citation

  • D. D. Sworder & J. E. Boyd, 1997. "Hybrid Estimation Algorithms, Part 2," Journal of Optimization Theory and Applications, Springer, vol. 94(1), pages 1-21, July.
    • Handle: RePEc:spr:joptap:v:94:y:1997:i:1:d:10.1023_a:1022618500543
    DOI: 10.1023/A:1022618500543
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