Optimal linear estimation with square-based sampling

Haomiao Zhou; Zhihong Deng; Yuanqing Xia; Mengyin Fu

doi:10.1016/j.ins.2014.10.016

Optimal linear estimation with square-based sampling

Haomiao Zhou, Zhihong Deng^*, Yuanqing Xia, Mengyin Fu

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper is concerned with the problem of event-based sampling for the purpose of reducing the number of data transferred in control systems and the corresponding modified Kalman filter. A mathematical description based upon square-based sampling is proposed, in which the sampling occured when the accumulated difference between two adjacent sampling points satisfies the specified event condition. Then, by utilizing the square based sampling method, a modified Kalman filter algorithm is adopted, in which the measurement is sent when the output exceeds a given event. The simulation results illustrate the advantage of the new event-based sampling scheme and the effectiveness of modified Kalman filter.

Original language	English
Pages (from-to)	225-236
Number of pages	12
Journal	Information Sciences
Volume	298
DOIs	https://doi.org/10.1016/j.ins.2014.10.016
Publication status	Published - 20 Mar 2015

Keywords

Event-based sampling
Kalman filtering
Squared-based state estimation

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10.1016/j.ins.2014.10.016

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title = "Optimal linear estimation with square-based sampling",

abstract = "This paper is concerned with the problem of event-based sampling for the purpose of reducing the number of data transferred in control systems and the corresponding modified Kalman filter. A mathematical description based upon square-based sampling is proposed, in which the sampling occured when the accumulated difference between two adjacent sampling points satisfies the specified event condition. Then, by utilizing the square based sampling method, a modified Kalman filter algorithm is adopted, in which the measurement is sent when the output exceeds a given event. The simulation results illustrate the advantage of the new event-based sampling scheme and the effectiveness of modified Kalman filter.",

keywords = "Event-based sampling, Kalman filtering, Squared-based state estimation",

author = "Haomiao Zhou and Zhihong Deng and Yuanqing Xia and Mengyin Fu",

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AU - Zhou, Haomiao

AU - Deng, Zhihong

AU - Xia, Yuanqing

AU - Fu, Mengyin

PY - 2015/3/20

Y1 - 2015/3/20

N2 - This paper is concerned with the problem of event-based sampling for the purpose of reducing the number of data transferred in control systems and the corresponding modified Kalman filter. A mathematical description based upon square-based sampling is proposed, in which the sampling occured when the accumulated difference between two adjacent sampling points satisfies the specified event condition. Then, by utilizing the square based sampling method, a modified Kalman filter algorithm is adopted, in which the measurement is sent when the output exceeds a given event. The simulation results illustrate the advantage of the new event-based sampling scheme and the effectiveness of modified Kalman filter.

AB - This paper is concerned with the problem of event-based sampling for the purpose of reducing the number of data transferred in control systems and the corresponding modified Kalman filter. A mathematical description based upon square-based sampling is proposed, in which the sampling occured when the accumulated difference between two adjacent sampling points satisfies the specified event condition. Then, by utilizing the square based sampling method, a modified Kalman filter algorithm is adopted, in which the measurement is sent when the output exceeds a given event. The simulation results illustrate the advantage of the new event-based sampling scheme and the effectiveness of modified Kalman filter.

KW - Event-based sampling

KW - Kalman filtering

KW - Squared-based state estimation

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DO - 10.1016/j.ins.2014.10.016

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SN - 0020-0255

VL - 298

SP - 225

EP - 236

JO - Information Sciences

JF - Information Sciences

ER -

Optimal linear estimation with square-based sampling

Abstract

Keywords

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