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Simulation-based optimal sensor scheduling with application to observer trajectory planning
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DOI：10.1016/j.automatica.2006.11.019.png)
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Abstract
En 中文
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. (c) 2007 Elsevier Ltd. All rights reserved.
Keywords:
sequential Monte Carlo
particle filter
stochastic approximation
stochastic control
sensor scheduling
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