Optimal Estimator for Distributed Anonymous Observers

Q. Li () and W. S. Wong ()
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Q. Li: Hong Kong Applied Science and Technology Research Institute Company Limited
W. S. Wong: Chinese University of Hong Kong

Journal of Optimization Theory and Applications, 2009, vol. 140, issue 1, No 4, 55-75

Abstract: 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.

Keywords: Distributed estimation; Stochastic approximation; Kiefer-Wolfowitz algorithm (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10957-008-9466-3

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