Simulation-based optimal sensor scheduling with application to observer trajectory planning
Sumeetpal S Singh, Nikolaos Kantas, Ba-Ngu Vo, Arnaud Doucet, Robin J Evans
AUTOMATICA | PERGAMON-ELSEVIER SCIENCE LTD | Published : 2007
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. © 2007 Elsevier Ltd. All rights reserved.