Least Square Fault Estimation for a Class of Sensor Networks

Yang Liu; Zidong Wang; Xiao He; Donghua Zhou

doi:10.1109/CCDC.2013.6561836

Least Square Fault Estimation for a Class of Sensor Networks

Yang Liu, Zidong Wang, Xiao He, Donghua Zhou

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

1 Citation (Scopus)

Abstract

In this paper, the problem of fault estimation is considered for a class of time-varying systems through a sensor network. The measurements collected via the sensor network are subject to probabilistic data missing. A set of least square estimators are designed for the addressed systems such that the estimation error variance is minimized at each tim step. By solving a set of Riccati-like matrix equations, the parameters of the desired estimators are calculated recursively. Numerical simulations are exploited to illustrate the effectiveness of the proposed algorithm.

Original language	English
Title of host publication	2013 25th Chinese Control and Decision Conference, CCDC
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4984-4988
Number of pages	5
ISBN (Electronic)	9781467355346, 9781467355322
ISBN (Print)	9781467355339
DOIs	https://doi.org/10.1109/CCDC.2013.6561836
Publication status	Published - 18 Jul 2013
Externally published	Yes
Event	25th Chinese Control and Decision Conference - Guiyang, China Duration: 25 May 2013 → 27 May 2013 Conference number: 25

Publication series

Name	Chinese Control and Decision Conference
Publisher	IEEE
Volume	2013
ISSN (Print)	1948-9439
ISSN (Electronic)	1948-9447

Conference

Conference	25th Chinese Control and Decision Conference
Abbreviated title	CCDC 2013
Country/Territory	China
City	Guiyang
Period	25/05/13 → 27/05/13

Access to Document

10.1109/CCDC.2013.6561836Licence: Unspecified

Cite this

@inproceedings{0a728fc56ed944ed9044c99bbf096e95,

title = "Least Square Fault Estimation for a Class of Sensor Networks",

abstract = "In this paper, the problem of fault estimation is considered for a class of time-varying systems through a sensor network. The measurements collected via the sensor network are subject to probabilistic data missing. A set of least square estimators are designed for the addressed systems such that the estimation error variance is minimized at each tim step. By solving a set of Riccati-like matrix equations, the parameters of the desired estimators are calculated recursively. Numerical simulations are exploited to illustrate the effectiveness of the proposed algorithm.",

keywords = "fault estimation, least square estimation, missing measurements, sensor network",

author = "Yang Liu and Zidong Wang and Xiao He and Donghua Zhou",

year = "2013",

month = jul,

day = "18",

doi = "10.1109/CCDC.2013.6561836",

language = "English",

isbn = "9781467355339",

series = "Chinese Control and Decision Conference",

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

pages = "4984--4988",

booktitle = "2013 25th Chinese Control and Decision Conference, CCDC",

address = "United States",

note = "25th Chinese Control and Decision Conference, CCDC 2013 ; Conference date: 25-05-2013 Through 27-05-2013",

}

Liu, Y, Wang, Z, He, X & Zhou, D 2013, Least Square Fault Estimation for a Class of Sensor Networks. in 2013 25th Chinese Control and Decision Conference, CCDC ., 6561836, Chinese Control and Decision Conference, vol. 2013, Institute of Electrical and Electronics Engineers Inc., pp. 4984-4988, 25th Chinese Control and Decision Conference, Guiyang, China, 25/05/13. https://doi.org/10.1109/CCDC.2013.6561836

Least Square Fault Estimation for a Class of Sensor Networks. / Liu, Yang; Wang, Zidong; He, Xiao et al.

2013 25th Chinese Control and Decision Conference, CCDC . Institute of Electrical and Electronics Engineers Inc., 2013. p. 4984-4988 6561836 (Chinese Control and Decision Conference; Vol. 2013).

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

TY - GEN

T1 - Least Square Fault Estimation for a Class of Sensor Networks

AU - Liu, Yang

AU - Wang, Zidong

AU - He, Xiao

AU - Zhou, Donghua

N1 - Conference code: 25

PY - 2013/7/18

Y1 - 2013/7/18

N2 - In this paper, the problem of fault estimation is considered for a class of time-varying systems through a sensor network. The measurements collected via the sensor network are subject to probabilistic data missing. A set of least square estimators are designed for the addressed systems such that the estimation error variance is minimized at each tim step. By solving a set of Riccati-like matrix equations, the parameters of the desired estimators are calculated recursively. Numerical simulations are exploited to illustrate the effectiveness of the proposed algorithm.

AB - In this paper, the problem of fault estimation is considered for a class of time-varying systems through a sensor network. The measurements collected via the sensor network are subject to probabilistic data missing. A set of least square estimators are designed for the addressed systems such that the estimation error variance is minimized at each tim step. By solving a set of Riccati-like matrix equations, the parameters of the desired estimators are calculated recursively. Numerical simulations are exploited to illustrate the effectiveness of the proposed algorithm.

KW - fault estimation

KW - least square estimation

KW - missing measurements

KW - sensor network

UR - http://www.scopus.com/inward/record.url?scp=84882782152&partnerID=8YFLogxK

U2 - 10.1109/CCDC.2013.6561836

DO - 10.1109/CCDC.2013.6561836

M3 - Conference contribution

AN - SCOPUS:84882782152

SN - 9781467355339

T3 - Chinese Control and Decision Conference

SP - 4984

EP - 4988

BT - 2013 25th Chinese Control and Decision Conference, CCDC

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 25th Chinese Control and Decision Conference

Y2 - 25 May 2013 through 27 May 2013

ER -

Least Square Fault Estimation for a Class of Sensor Networks

Abstract

Publication series

Conference

Access to Document

Other files and links

Cite this