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

被引：32
|
作者：
Singh, Sumeetpal S.
Kantas, Nikolaos
Vo, Ba-Ngu
Doucet, Arnaud
Evans, Robin J.
机构：
[1] Univ Cambridge, Dept Engn, Signal Proc Grp, Cambridge CB2 1PZ, England
[2] Univ Melbourne, Dept Elect & Elect Engn, Melbourne, Vic 3010, Australia
[3] Univ British Columbia, Dept Comp Sci, Vancouver, BC V6T 1W5, Canada
[4] Univ British Columbia, Dept Stat, Vancouver, BC V6T 1W5, Canada
来源：
AUTOMATICA | 2007年 / 43卷 / 05期
关键词：
sequential Monte Carlo; particle filter; stochastic approximation; stochastic control; sensor scheduling;
D O I：
10.1016/j.automatica.2006.11.019
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：817 / 830
页数：14
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