Sparsity-promoting extended kalman filtering for target tracking in wireless sensor networks

Research output: Contribution to journalArticle

54 Scopus citations

Abstract

In this letter, we study the problem of target tracking based on energy readings of sensors. We minimize the estimation error by using an extended Kalman filter (EKF). The Kalman gain matrix is obtained as the solution to an optimization problem in which a sparsity-promoting penalty function is added to the objective. The added term penalizes the number of nonzero columns of the Kalman gain matrix, which corresponds to the number of active sensors. By using a sparse Kalman gain matrix only a few sensors send their measurements to the fusion center, thereby saving energy. Simulation results show that an EKF with a sparse Kalman gain matrix can achieve tracking performance that is very close to that of the classical EKF, where all sensors transmit to the fusion center.

Original languageEnglish (US)
Article number6310013
Pages (from-to)845-848
Number of pages4
JournalIEEE Signal Processing Letters
Volume19
Issue number12
DOIs
StatePublished - Oct 29 2012

Keywords

  • Alternating directions method of multipliers
  • extended Kalman filter
  • sensor selection
  • sparsity-promoting optimization
  • target tracking
  • wireless sensor networks

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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