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 › peer-review
Author(s)
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Detail(s)
Original language | English |
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Pages (from-to) | 4276-4283 |
Journal / Publication | IEEE Transactions on Automatic Control |
Volume | 64 |
Issue number | 10 |
Online published | 22 Jan 2019 |
Publication status | Published - Oct 2019 |
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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 › peer-review