Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel

Jiarao Huang; Dawei Shi; Tongwen Chen

doi:10.1109/CDC.2016.7799052

Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel

Jiarao Huang, Dawei Shi, Tongwen Chen

School of Automation

University of Alberta

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

In this work, a problem of event-based state estimation for hidden Markov models is investigated. We consider the scenario that the transmission of the sensor measurement is decided by a dynamic event-trigger, the state of which depends on both the sensor measurement and the previous triggering state. An independent and identically distributed Bernoulli process is utilized to model the effect of packet dropout. Using the reference probability measure approach, expressions for the unnormalized and normalized conditional probability distributions of the states on the event-triggered measurement information are derived, based on which optimal event-based state estimates can be obtained. The effectiveness of the proposed results is illustrated through a numerical example together with comparative simulations.

Original language	English
Title of host publication	2016 IEEE 55th Conference on Decision and Control, CDC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5122-5127
Number of pages	6
ISBN (Electronic)	9781509018376
DOIs	https://doi.org/10.1109/CDC.2016.7799052
Publication status	Published - 27 Dec 2016
Event	55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States Duration: 12 Dec 2016 → 14 Dec 2016

Publication series

Name	2016 IEEE 55th Conference on Decision and Control, CDC 2016

Conference

Conference	55th IEEE Conference on Decision and Control, CDC 2016
Country/Territory	United States
City	Las Vegas
Period	12/12/16 → 14/12/16

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10.1109/CDC.2016.7799052

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Huang, J., Shi, D., & Chen, T. (2016). Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 5122-5127). Article 7799052 (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7799052

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title = "Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel",

abstract = "In this work, a problem of event-based state estimation for hidden Markov models is investigated. We consider the scenario that the transmission of the sensor measurement is decided by a dynamic event-trigger, the state of which depends on both the sensor measurement and the previous triggering state. An independent and identically distributed Bernoulli process is utilized to model the effect of packet dropout. Using the reference probability measure approach, expressions for the unnormalized and normalized conditional probability distributions of the states on the event-triggered measurement information are derived, based on which optimal event-based state estimates can be obtained. The effectiveness of the proposed results is illustrated through a numerical example together with comparative simulations.",

author = "Jiarao Huang and Dawei Shi and Tongwen Chen",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 55th IEEE Conference on Decision and Control, CDC 2016 ; Conference date: 12-12-2016 Through 14-12-2016",

year = "2016",

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doi = "10.1109/CDC.2016.7799052",

language = "English",

series = "2016 IEEE 55th Conference on Decision and Control, CDC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Huang, J, Shi, D & Chen, T 2016, Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel. in 2016 IEEE 55th Conference on Decision and Control, CDC 2016., 7799052, 2016 IEEE 55th Conference on Decision and Control, CDC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 5122-5127, 55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 12/12/16. https://doi.org/10.1109/CDC.2016.7799052

Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel. / Huang, Jiarao; Shi, Dawei; Chen, Tongwen.
2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 5122-5127 7799052 (2016 IEEE 55th Conference on Decision and Control, CDC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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PY - 2016/12/27

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N2 - In this work, a problem of event-based state estimation for hidden Markov models is investigated. We consider the scenario that the transmission of the sensor measurement is decided by a dynamic event-trigger, the state of which depends on both the sensor measurement and the previous triggering state. An independent and identically distributed Bernoulli process is utilized to model the effect of packet dropout. Using the reference probability measure approach, expressions for the unnormalized and normalized conditional probability distributions of the states on the event-triggered measurement information are derived, based on which optimal event-based state estimates can be obtained. The effectiveness of the proposed results is illustrated through a numerical example together with comparative simulations.

AB - In this work, a problem of event-based state estimation for hidden Markov models is investigated. We consider the scenario that the transmission of the sensor measurement is decided by a dynamic event-trigger, the state of which depends on both the sensor measurement and the previous triggering state. An independent and identically distributed Bernoulli process is utilized to model the effect of packet dropout. Using the reference probability measure approach, expressions for the unnormalized and normalized conditional probability distributions of the states on the event-triggered measurement information are derived, based on which optimal event-based state estimates can be obtained. The effectiveness of the proposed results is illustrated through a numerical example together with comparative simulations.

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M3 - Conference contribution

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BT - 2016 IEEE 55th Conference on Decision and Control, CDC 2016

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 55th IEEE Conference on Decision and Control, CDC 2016

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ER -

Huang J, Shi D, Chen T. Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 5122-5127. 7799052. (2016 IEEE 55th Conference on Decision and Control, CDC 2016). doi: 10.1109/CDC.2016.7799052

Dynamically event-triggered state estimation of hidden Markov models through a lossy communication channel

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