Simulation-based optimal sensor scheduling with application to observer trajectory planning

Authors:
Sumeetpal S. Singh;Nikolaos Kantas;Ba-Ngu Vo;Arnaud Doucet;Robin J. Evans
Affiliations:
Signal Processing Group, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, UK;Signal Processing Group, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, UK;Department of Electrical and Electronic Engineering, University of Melbourne, Vic. 3010, Australia;Department of Computer Science, University of British Columbia, Canada and Department of Statistics, University of British Columbia, Canada;Department of Electrical and Electronic Engineering, University of Melbourne, Vic. 3010, Australia
Venue:
Automatica (Journal of IFAC)
Year:
2007

Citing 7
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Abstract

The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous. This general case is important as it is the natural framework for many applications. The aim is to minimise the variance of the estimation error of the hidden state w.r.t. the action sequence. We present a novel simulation-based method that uses a stochastic gradient algorithm to find optimal actions.