Even Triggered Risk-Sensitive State Estimation for Hidden Markov Models

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Detail(s)

Original languageEnglish
Pages (from-to)4276-4283
Journal / PublicationIEEE Transactions on Automatic Control
Volume64
Issue number10
Online published22 Jan 2019
Publication statusPublished - Oct 2019

Abstract

An event-triggered risk-sensitive state estimation problem for hidden Markov models is investigated in this work. The event-triggered scheme considered is fairly general, which covers most existing event-triggered conditions. By utilizing the reference probability measure approach, this estimation problem is reformulated as an equivalent one and solved. We show that the event-triggered risk-sensitive maximum a posteriori probability estimates can be obtained based on a newly defined unnormalized information state, which has a linear recursive form. Furthermore, the explicit solutions for two major classes of event-triggered conditions are derived if the measurement noise is Gaussian. A numerical comparison is provided to illustrate the effectiveness of the proposed results.

Research Area(s)

  • Event-triggered state estimation, hidden Markov models (HMMs), maximum a posteriori probability (MAP), risk sensitive, SYSTEMS

Citation Format(s)

Even Triggered Risk-Sensitive State Estimation for Hidden Markov Models. / Xu, Jiapeng; Ho, Daniel W. C.; Li, Fangfei; Yang, Wen; Tang, Yang.

In: IEEE Transactions on Automatic Control, Vol. 64, No. 10, 10.2019, p. 4276-4283.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal