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Optimal Estimator for Distributed Anonymous Observers

Author

Listed:
  • Q. Li

    (Hong Kong Applied Science and Technology Research Institute Company Limited)

  • W. S. Wong

    (Chinese University of Hong Kong)

Abstract

In this paper, we consider a distributed estimation problem in which multiple observations of a signal process are combined via the maximum function for the decision making. A key result established is that, under suitable technical conditions, the optimal decision function can be implemented by means of thresholds. A natural question is how to determine the optimal threshold value. We propose here an algorithm for threshold adjustment by means of training sequences. The algorithm is a variation of the Kiefer-Wolfowitz algorithm with expanding truncations and randomized differences. A result of the paper is to establish the convergence of the algorithm if the variance of observation noises is small enough.

Suggested Citation

  • Q. Li & W. S. Wong, 2009. "Optimal Estimator for Distributed Anonymous Observers," Journal of Optimization Theory and Applications, Springer, vol. 140(1), pages 55-75, January.
    • Handle: RePEc:spr:joptap:v:140:y:2009:i:1:d:10.1007_s10957-008-9466-3
    DOI: 10.1007/s10957-008-9466-3
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    Cited by:

    1. Shreyas Vathul Subramanian & Daniel A. DeLaurentis & Dengfeng Sun, 2016. "Dual Averaging with Adaptive Random Projection for Solving Evolving Distributed Optimization Problems," Journal of Optimization Theory and Applications, Springer, vol. 170(2), pages 493-511, August.

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