Optimal periodic sensor scheduling in networks of dynamical systems

Sijia Liu, Makan Fardad, Pramod K. Varshney, Engin Masazade

Research output: Contribution to journalArticle

58 Scopus citations

Abstract

We consider the problem of finding optimal time-periodic sensor schedules for estimating the state of discrete-time dynamical systems. We assume that multiple sensors have been deployed and that the sensors are subject to resource constraints, which limits the number of times each can be activated over one period of the periodic schedule. We seek an algorithm that strikes a balance between estimation accuracy and total sensor activations over one period. We make a correspondence between active sensors and the nonzero columns of the estimator gain. We formulate an optimization problem in which we minimize the trace of the error covariance with respect to the estimator gain while simultaneously penalizing the number of nonzero columns of the estimator gain. This optimization problem is combinatorial in nature, and we employ the alternating direction method of multipliers (ADMM) to find its locally optimal solutions. Numerical results and comparisons with other sensor scheduling algorithms in the literature are provided to illustrate the effectiveness of our proposed method.

Original languageEnglish (US)
Article number6805223
Pages (from-to)3055-3068
Number of pages14
JournalIEEE Transactions on Signal Processing
Volume62
Issue number12
DOIs
StatePublished - Jun 15 2014

Keywords

  • Alternating direction method of multipliers
  • dynamical systems
  • optimization
  • sensor networks
  • sensor scheduling
  • sparsity
  • state estimation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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