A sub-optimal sensor scheduling strategy using convex optimization

Chong Li, Nicola Elia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, we consider a sub-optimal off-line stochastic scheduling of a single sensor that visits (measures) one site, modeled as a discrete-time linear time-invariant (DTLTI) dynamic system, at each time instant with the objective to minimize certain measure of the estimation error. The objective of this paper is to search the optimal probability distributions under two cost functions. We show that the optimal scheduling distribution is computable by solving a quasi-convex optimization problem in the case we focus on the minimization of maximal estimate error among sites. When the cost function is the average estimate error of all sites, the scheduling problem for a set of special DTLTI systems can be casted and efficiently solved as a convex optimization problem by exploiting the structure of the underlying Riccati-like equation. Furthermore, we propose a deterministic scheduling strategy based on the optimal stochastic one. Finally, we show some simulation results to verify our strategies.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages3603-3608
Number of pages6
StatePublished - Sep 29 2011
Externally publishedYes
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Publication series

NameProceedings of the American Control Conference
ISSN (Print)0743-1619

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

Keywords

  • Kalman Filter
  • Linear Matrix Inequality
  • Quasi-convexity
  • Riccati-like Equation

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