Singh, SS and Kantas, N and Vo, B-N and Doucet, A and Evans, RJ (2007) Simulation-based optimal sensor scheduling with application to observer trajectory planning. Automatica, 43. pp. 817-830. ISSN 0005-1098Full text not available from this repository.
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.
|Uncontrolled Keywords:||Particle filter Sensor scheduling Sequential Monte Carlo Stochastic approximation Stochastic control|
|Divisions:||Div F > Signal Processing and Communications|
|Depositing User:||Cron Job|
|Date Deposited:||07 Mar 2014 11:21|
|Last Modified:||08 Dec 2014 02:27|