Fusion Kalman and Weighted UFIR State Estimator with Improved Accuracy

Sung Hyun You; Choon Ki Ahn; Yuriy S. Shmaliy; Shunyi Zhao

doi:10.1109/TIE.2019.2958278

Fusion Kalman and Weighted UFIR State Estimator with Improved Accuracy

Sung Hyun You, Choon Ki Ahn, Yuriy S. Shmaliy, Shunyi Zhao

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

7 Citations (Scopus)

Abstract

In this article, estimates of the Kalman filter (KF) and weighted unbiased finite impulse response (UFIR) filter are fused in discrete time-varying state-space to improve the robustness in uncertain environments associated with industrial applications. The weighted UFIR filter is derived using the Frobenius norm and termed as Frobenius finite impulse response (FFIR) filter. It is confirmed that the FFIR filter has better performance under the uncertainties and errors in the noise statistics, while the KF filter is best when the model and noise are exactly known. Based on a numerical example of a hover system, we show that the FFIR filter is able to outperform the UFIR filter and that the fusion KF/FFIR filter is able to outperform both of them. An experimental verification provided for the drone velocity estimation under the hover operation conditions has proved a better accuracy and robustness of the proposed fusion KF/FFIR filter.

Original language	English
Article number	8931678
Pages (from-to)	10713-10722
Number of pages	10
Journal	IEEE Transactions on Industrial Electronics
Volume	67
Issue number	12
DOIs	https://doi.org/10.1109/TIE.2019.2958278
Publication status	Published - 2020 Dec

Bibliographical note

Funding Information:
Manuscript received December 12, 2018; revised May 12, 2019 and September 17, 2019; accepted November 12, 2019. Date of publication December 12, 2019; date of current version August 18, 2020. This work was supported in part by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea under Grant 20174030201820 and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by the National Natural Science Foundation of China under Grant 61973136. (Corresponding author: Choon Ki Ahn.) S. H. You and C. K. Ahn are with the School of Electrical Engineering, Korea University, Seoul 136-701, South Korea (e-mail: hironaka@korea.ac.kr).

Publisher Copyright:
© 1982-2012 IEEE.

Keywords

Frobenius norm
Kalman filter (KF)
fusion filter
unbiased finite impulse response (FIR) filter

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/TIE.2019.2958278

Cite this

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title = "Fusion Kalman and Weighted UFIR State Estimator with Improved Accuracy",

abstract = "In this article, estimates of the Kalman filter (KF) and weighted unbiased finite impulse response (UFIR) filter are fused in discrete time-varying state-space to improve the robustness in uncertain environments associated with industrial applications. The weighted UFIR filter is derived using the Frobenius norm and termed as Frobenius finite impulse response (FFIR) filter. It is confirmed that the FFIR filter has better performance under the uncertainties and errors in the noise statistics, while the KF filter is best when the model and noise are exactly known. Based on a numerical example of a hover system, we show that the FFIR filter is able to outperform the UFIR filter and that the fusion KF/FFIR filter is able to outperform both of them. An experimental verification provided for the drone velocity estimation under the hover operation conditions has proved a better accuracy and robustness of the proposed fusion KF/FFIR filter. ",

keywords = "Frobenius norm, Kalman filter (KF), fusion filter, unbiased finite impulse response (FIR) filter",

author = "You, {Sung Hyun} and Ahn, {Choon Ki} and Shmaliy, {Yuriy S.} and Shunyi Zhao",

note = "Funding Information: Manuscript received December 12, 2018; revised May 12, 2019 and September 17, 2019; accepted November 12, 2019. Date of publication December 12, 2019; date of current version August 18, 2020. This work was supported in part by “Human Resources Program in Energy Technology” of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) granted financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea under Grant 20174030201820 and in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by the National Natural Science Foundation of China under Grant 61973136. (Corresponding author: Choon Ki Ahn.) S. H. You and C. K. Ahn are with the School of Electrical Engineering, Korea University, Seoul 136-701, South Korea (e-mail: hironaka@korea.ac.kr). Publisher Copyright: {\textcopyright} 1982-2012 IEEE.",

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Fusion Kalman and Weighted UFIR State Estimator with Improved Accuracy

Abstract

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Keywords

ASJC Scopus subject areas

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