Data-driven power control for state estimation: A Bayesian inference approach

• Junfeng Wu ^[1] ; Yuzhe Li ^[1] ; Daniel E. Quevedo ^[2]
  1. [1] Hong Kong University of Science and Technology

     Hong Kong University of Science and Technology

     RAE de Hong Kong (China)

     
  2. [2] School of Electronic Engineering and Computer Science, University of Newcastle, Australia
• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 54, 2015, págs. 332-339
• Idioma: inglés
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• Resumen

  • We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops. As related to packet dropout rate, transmission power is chosen by the sensor based on the relative importance of the local state estimate. The proposed power controller is proved to preserve Gaussianity of local estimate innovation, which enables us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative non-data-driven controllers demonstrate performance improvement using our approach.