New receding horizon fir estimator for blind smart sensing of velocity via position measurements

Choon Ki Ahn; Yuriy S. Shmaliy

doi:10.1109/TCSII.2017.2707123

New receding horizon fir estimator for blind smart sensing of velocity via position measurements

Choon Ki Ahn, Yuriy S. Shmaliy

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

14 Citations (Scopus)

Abstract

Smart sensors often require that embedded estimators are robust and blind for given averaging horizons. This brief proposes a new receding horizon (RH) finite impulse response (FIR) velocity estimator that fits these needs by utilizing data from N recent discrete position measurements with fading weights. The conventional Kalman estimator typically exhibits poor performance and may even diverge under imprecisely defined noise statistics and/or numerical errors. In contrast, the proposed weighted RH FIR estimator does not require any information about noise, which makes it more robust and blind for a given N. The weighted RH FIR estimator minimizes the effects of uncertainties caused by imprecisely defined noise statistics and/or numerical errors and demonstrates better robustness than the existing FIR estimators. We also discuss how to choose the optimal horizon size for the weighted RH FIR estimator. The better performance of the proposed weighted RH FIR estimator against the Kalman and FIR estimators is shown through simulations under diverse operation conditions.

Original language	English
Pages (from-to)	135-139
Number of pages	5
Journal	IEEE Transactions on Circuits and Systems II: Express Briefs
Volume	65
Issue number	1
DOIs	https://doi.org/10.1109/TCSII.2017.2707123
Publication status	Published - 2018 Jan

Bibliographical note

Funding Information:
NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325

Funding Information:
Manuscript received March 18, 2017; accepted May 18, 2017. Date of publication May 23, 2017; date of current version December 22, 2017. This work was supported by NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325. This brief was recommended by Associate Editor H.-T. Zhang. (Corresponding author: Choon Ki Ahn.) C. K. Ahn is with the School of Electrical Engineering, Korea University, Seoul 136-701, South Korea (e-mail: [email protected]).

Publisher Copyright:
© 2017 IEEE. Personal use is permitted.

Keywords

Blind operation
Fading weight
Kalman estimator
Receding horizon
Robustness
Velocity estimation

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/TCSII.2017.2707123

Cite this

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title = "New receding horizon fir estimator for blind smart sensing of velocity via position measurements",

abstract = "Smart sensors often require that embedded estimators are robust and blind for given averaging horizons. This brief proposes a new receding horizon (RH) finite impulse response (FIR) velocity estimator that fits these needs by utilizing data from N recent discrete position measurements with fading weights. The conventional Kalman estimator typically exhibits poor performance and may even diverge under imprecisely defined noise statistics and/or numerical errors. In contrast, the proposed weighted RH FIR estimator does not require any information about noise, which makes it more robust and blind for a given N. The weighted RH FIR estimator minimizes the effects of uncertainties caused by imprecisely defined noise statistics and/or numerical errors and demonstrates better robustness than the existing FIR estimators. We also discuss how to choose the optimal horizon size for the weighted RH FIR estimator. The better performance of the proposed weighted RH FIR estimator against the Kalman and FIR estimators is shown through simulations under diverse operation conditions.",

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author = "Ahn, {Choon Ki} and Shmaliy, {Yuriy S.}",

note = "Funding Information: NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325 Funding Information: Manuscript received March 18, 2017; accepted May 18, 2017. Date of publication May 23, 2017; date of current version December 22, 2017. This work was supported by NRF through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325. This brief was recommended by Associate Editor H.-T. Zhang. (Corresponding author: Choon Ki Ahn.) C. K. Ahn is with the School of Electrical Engineering, Korea University, Seoul 136-701, South Korea (e-mail: [email protected]). Publisher Copyright: {\textcopyright} 2017 IEEE. Personal use is permitted.",

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New receding horizon fir estimator for blind smart sensing of velocity via position measurements

Abstract

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Keywords

ASJC Scopus subject areas

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