Event-based state estimation of discrete-state hidden Markov models

• Dawei Shi ^[1] ; Robert J. Elliott ^[2] ; Tongwen Chen ^[3]
  1. [1] Beijing Institute of Technology

     Beijing Institute of Technology

     China

     
  2. [2] University of Adelaide

     University of Adelaide

     Australia

     
  3. [3] University of Alberta

     University of Alberta

     Canadá

     

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• Localización: Automatica: A journal of IFAC the International Federation of Automatic Control, ISSN 0005-1098, Vol. 65, 2016, págs. 12-26
• Idioma: inglés
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• Resumen
  • español

    Event-based estimation;

  • English

    The state estimation problem for hidden Markov models subject to event-based sensor measurement updates is considered in this work, using the change of probability approach. We assume the measurement updates are transmitted through wired or wireless communication networks. For the scenarios with reliable and unreliable communication channels, analytical expressions for the probability distributions of the states conditioned on all the past point- and set-valued measurement information are obtained. Also, we show that the scenario with a lossy channel, but without the event-trigger, can be treated as a special case of the reliable channel results. Based on these results, closed-form expressions for the estimated communication rates under the original probability measure are presented, which are shown to be the ratio between a weighted 11-norm and the 11-norm of the unnormalized conditional probability distributions of the states under the new probability measures constructed. Implementation issues are discussed, and the effectiveness of the results is illustrated by numerical examples and comparative simulations.