Nonlinear event-based state estimation using sequential Monte Carlo approach

Shaikshavali Chitraganti, Mohamed A.H. Darwish

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

State estimation of nonlinear stochastic system in the setting of event-based (EB) measurements is quite challenging, because the measurements are not available at each sampling period, but are available only when a certain pre-specified event occurs. Recently, a nonlinear EB state estimator using Sequential Monte-Carlo approach is proposed in [21], where the authors obtained a EB state estimator for a given threshold. In this work, the results of [21] are extended as follows. Firstly, an empirical relation between the EB threshold and the average communication rate is obtained. Then, the performance of the estimator is evaluated by comparing the approximate error covariance matrix with the posterior Cramér-Rao bound. In addition, the computational complexity is addressed using the equivalent flop measure. Finally, the effectiveness of the proposed approach is demonstrated using two simulation examples.

LanguageEnglish
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages2170-2175
Number of pages6
ISBN (Electronic)978-1-5090-2873-3
ISBN (Print)978-1-5090-2874-0
DOIs
StatePublished - 18 Jan 2018
Event56th IEEE Conference on Decision and Control (CDC 2017) - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017
Conference number: 56
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8253407

Conference

Conference56th IEEE Conference on Decision and Control (CDC 2017)
Abbreviated titleCDC 2017
CountryAustralia
CityMelbourne
Period12/12/1715/12/17
Internet address

Fingerprint

Sequential Monte Carlo
State Estimation
State estimation
Cramer-Rao bounds
Stochastic systems
Covariance matrix
Computational complexity
Estimator
Sampling
Communication
Cramér-Rao Bound
Nonlinear Stochastic Systems
Computational Complexity
Simulation

Cite this

Chitraganti, S., & Darwish, M. A. H. (2018). Nonlinear event-based state estimation using sequential Monte Carlo approach. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017 (pp. 2170-2175). Piscataway: Institute of Electrical and Electronics Engineers. DOI: 10.1109/CDC.2017.8263966
Chitraganti, Shaikshavali ; Darwish, Mohamed A.H./ Nonlinear event-based state estimation using sequential Monte Carlo approach. 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Piscataway : Institute of Electrical and Electronics Engineers, 2018. pp. 2170-2175
@inproceedings{1dfed844e2e54e9daf0be6d4df29e8cf,
title = "Nonlinear event-based state estimation using sequential Monte Carlo approach",
abstract = "State estimation of nonlinear stochastic system in the setting of event-based (EB) measurements is quite challenging, because the measurements are not available at each sampling period, but are available only when a certain pre-specified event occurs. Recently, a nonlinear EB state estimator using Sequential Monte-Carlo approach is proposed in [21], where the authors obtained a EB state estimator for a given threshold. In this work, the results of [21] are extended as follows. Firstly, an empirical relation between the EB threshold and the average communication rate is obtained. Then, the performance of the estimator is evaluated by comparing the approximate error covariance matrix with the posterior Cram{\'e}r-Rao bound. In addition, the computational complexity is addressed using the equivalent flop measure. Finally, the effectiveness of the proposed approach is demonstrated using two simulation examples.",
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Chitraganti, S & Darwish, MAH 2018, Nonlinear event-based state estimation using sequential Monte Carlo approach. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Institute of Electrical and Electronics Engineers, Piscataway, pp. 2170-2175, 56th IEEE Conference on Decision and Control (CDC 2017), Melbourne, Australia, 12/12/17. DOI: 10.1109/CDC.2017.8263966

Nonlinear event-based state estimation using sequential Monte Carlo approach. / Chitraganti, Shaikshavali; Darwish, Mohamed A.H.

2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Piscataway : Institute of Electrical and Electronics Engineers, 2018. p. 2170-2175.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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N2 - State estimation of nonlinear stochastic system in the setting of event-based (EB) measurements is quite challenging, because the measurements are not available at each sampling period, but are available only when a certain pre-specified event occurs. Recently, a nonlinear EB state estimator using Sequential Monte-Carlo approach is proposed in [21], where the authors obtained a EB state estimator for a given threshold. In this work, the results of [21] are extended as follows. Firstly, an empirical relation between the EB threshold and the average communication rate is obtained. Then, the performance of the estimator is evaluated by comparing the approximate error covariance matrix with the posterior Cramér-Rao bound. In addition, the computational complexity is addressed using the equivalent flop measure. Finally, the effectiveness of the proposed approach is demonstrated using two simulation examples.

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Chitraganti S, Darwish MAH. Nonlinear event-based state estimation using sequential Monte Carlo approach. In 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Piscataway: Institute of Electrical and Electronics Engineers. 2018. p. 2170-2175. Available from, DOI: 10.1109/CDC.2017.8263966