Paper Info
Title
Stochastic online sensor scheduler for remote state estimation
Abstract
In this paper, a remote state estimation problem where a sensor measures the state of a linear discrete-time process in an infinite time horizon is considered. We aim to minimize the average estimation error subject to a limited sensor-estimator communication rate. We propose a stochastic online sensor schedule: whether or not the sensor sends data is based on its measurements and a stochastic holding time between the present and the most recent sensor-estimator communication instance. This decision process is formulated as a generalized geometric programming (GGP) optimization problem. It can be solved with a tractable computational complexity and provides a better performance compared with the optimal offline schedule. Numerical example is provided to illustrate main results.
Year
DOI
Venue
2013
10.1109/CPSNA.2013.6614251
CPSNA
Keywords
Field
DocType
computational complexity,decision theory,discrete time systems,geometric programming,networked control systems,sensors,state estimation,stochastic systems,ggp optimization problem,average estimation error,remote state estimation problem,stochastic generalized geometric programming optimization problem,stochastic holding time,stochastic infinite time horizon,stochastic linear discrete-time process,stochastic online sensor scheduler,stochastic optimal offline scheduling,stochastic sensor measures,stochastic sensor-estimator communication instance,stochastic sensor-estimator communication rate,tractable computational complexity
Stochastic optimization,Mathematical optimization,Discrete-time stochastic process,Computer science,Stochastic neural network,Continuous-time stochastic process,Geometric programming,Stochastic programming,Stochastic approximation,Stochastic control
Conference
Citations 
PageRank 
References 
0
0.34
7
Authors
3
Name
Order
Citations
PageRank
Junfeng Wu142833.16
Yilin Mo289151.51
Ling Shi31717107.86