Sparsity-Promoting Extended Kalman Filtering for Target Tracking in Wireless Sensor Networks

MAŞAZADE E., Fardad M., Varshney P. K.

IEEE SIGNAL PROCESSING LETTERS, cilt.19, sa.12, ss.845-848, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19 Sayı: 12
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1109/lsp.2012.2220350
  • Dergi Adı: IEEE SIGNAL PROCESSING LETTERS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.845-848
  • Anahtar Kelimeler: Alternating directions method of multipliers, extended Kalman filter, sensor selection, sparsity-promoting optimization, target tracking, wireless sensor networks, SOURCE LOCALIZATION, SELECTION
  • Marmara Üniversitesi Adresli: Hayır

Özet

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.