On the Optimal Solutions of the Infinite-Horizon Linear Sensor Scheduling Problem

被引：52

作者：

Zhao, Lin ^{[1
]}

Zhang, Wei ^{[1
]}

Hu, Jianghai ^{[2
]}

Abate, Alessandro ^{[3
,4
]}

Tomlin, Claire J. ^{[5
]}

机构：

[1] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA

[2] Purdue Univ, Sch Elect & Comp Engn, W Lafayette, IN 47907 USA

[3] Univ Oxford, Dept Comp Sci, Oxford OX1 2JD, England

[4] Delft Univ Technol, Delft Ctr Syst & Control, NL-2628 CN Delft, Netherlands

[5] Univ Calif Berkeley, Dept Elect Engn & Comp Sci, Berkeley, CA 94720 USA

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2014年 / 59卷 / 10期

基金：

美国国家科学基金会;

关键词：

Average cost per stage; Kalman filter; networked control systems; sensor scheduling; SELECTION;

D O I：

10.1109/TAC.2014.2314222

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper studies the infinite-horizon sensor scheduling problem for linear Gaussian processes with linear measurement functions. Several important properties of the optimal infinite-horizon schedules are derived. In particular, it is proved that under some mild conditions, both the optimal infinite-horizon average-per-stage cost and the corresponding optimal sensor schedules are independent of the covariance matrix of the initial state. It is also proved that the optimal estimation cost can be approximated arbitrarily closely by a periodic schedule with a finite period. Moreover, it is shown that the sequence of the average-per-stage costs of the optimal schedule must converge. These theoretical results provide valuable insights into the design and analysis of various infinite-horizon sensor scheduling algorithms.

引用

页码：2825 / 2830

页数：6