Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics

Shunyi Zhao; Yuriy S. Shmaliy; Peng Shi; Choon Ki Ahn

doi:10.1109/TIE.2016.2636814

Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics

Shunyi Zhao, Yuriy S. Shmaliy, Peng Shi, Choon Ki Ahn

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

91 Citations (Scopus)

Abstract

In this paper, we fuse the Kalman filter (KF) that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal. The fusion filter employs the KF and UFIR filter as subfilters and produces smaller errors under the industrial conditions. In order to provide the best fusion effect, the operation point where UFIR meets Kalman is determined by applying probabilistic weights to each subfilter. Extensive simulations of the three degree of freedom (3-DOF) hover system have shown that the fusion filter output tends to range close to that by the best subfilter. Experimental verification provided for a 1-DOF torsion system has confirmed validity of simulation.

Original language	English
Article number	7778185
Pages (from-to)	3075-3083
Number of pages	9
Journal	IEEE Transactions on Industrial Electronics
Volume	64
Issue number	4
DOIs	https://doi.org/10.1109/TIE.2016.2636814
Publication status	Published - 2017 Apr

Bibliographical note

Funding Information:
This work was supported in part by the National Natural Science Foundation of China (61603155), in part by the 111 Project (B12018), and in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1006101).

Publisher Copyright:
© 2016 IEEE.

Keywords

Fusion filter (FF)
Kalman filter (KF)
industrial conditions
state estimation
unbiased finite-impulse response (UFIR) filter

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/TIE.2016.2636814

Cite this

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title = "Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics",

abstract = "In this paper, we fuse the Kalman filter (KF) that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal. The fusion filter employs the KF and UFIR filter as subfilters and produces smaller errors under the industrial conditions. In order to provide the best fusion effect, the operation point where UFIR meets Kalman is determined by applying probabilistic weights to each subfilter. Extensive simulations of the three degree of freedom (3-DOF) hover system have shown that the fusion filter output tends to range close to that by the best subfilter. Experimental verification provided for a 1-DOF torsion system has confirmed validity of simulation.",

keywords = "Fusion filter (FF), Kalman filter (KF), industrial conditions, state estimation, unbiased finite-impulse response (UFIR) filter",

author = "Shunyi Zhao and Shmaliy, {Yuriy S.} and Peng Shi and Ahn, {Choon Ki}",

note = "Funding Information: This work was supported in part by the National Natural Science Foundation of China (61603155), in part by the 111 Project (B12018), and in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1006101). Publisher Copyright: {\textcopyright} 2016 IEEE.",

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AU - Shi, Peng

AU - Ahn, Choon Ki

N1 - Funding Information: This work was supported in part by the National Natural Science Foundation of China (61603155), in part by the 111 Project (B12018), and in part by the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1006101). Publisher Copyright: © 2016 IEEE.

PY - 2017/4

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N2 - In this paper, we fuse the Kalman filter (KF) that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal. The fusion filter employs the KF and UFIR filter as subfilters and produces smaller errors under the industrial conditions. In order to provide the best fusion effect, the operation point where UFIR meets Kalman is determined by applying probabilistic weights to each subfilter. Extensive simulations of the three degree of freedom (3-DOF) hover system have shown that the fusion filter output tends to range close to that by the best subfilter. Experimental verification provided for a 1-DOF torsion system has confirmed validity of simulation.

AB - In this paper, we fuse the Kalman filter (KF) that is optimal but not robust with the unbiased finite-impulse response (UFIR) filter which is more robust than KF but not optimal. The fusion filter employs the KF and UFIR filter as subfilters and produces smaller errors under the industrial conditions. In order to provide the best fusion effect, the operation point where UFIR meets Kalman is determined by applying probabilistic weights to each subfilter. Extensive simulations of the three degree of freedom (3-DOF) hover system have shown that the fusion filter output tends to range close to that by the best subfilter. Experimental verification provided for a 1-DOF torsion system has confirmed validity of simulation.

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KW - Kalman filter (KF)

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Fusion Kalman/UFIR Filter for State Estimation With Uncertain Parameters and Noise Statistics

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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