Adaptive Kalman filter based on improved second order mutual difference estimation

Yixin Zhang; Hai Zhang

doi:10.1109/IAEAC.2015.7428612

Adaptive Kalman filter based on improved second order mutual difference estimation

Yixin Zhang
, Hai Zhang

School of Automation Science and Electrical Engineering

Beihang University

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

1 Scopus citations

Abstract

In this paper, a method to compute noise variance and adapt measurement noise covariance matrix R in Kalman filter is proposed. We construct a virtual redundant measurement using α-β-γ filter to apply the second order mutual difference estimation method, which estimate noise variance effectively, in single measurement to calculate noise variance. And statistical data selection algorithm is proposed to avoid inaccuracy caused by lag in the α-β-γ filter. Simulations indicate this method is effective in R adaption with relatively low computation.

Original language	English
Title of host publication	Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015
Editors	Bing Xu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	542-546
Number of pages	5
ISBN (Electronic)	9781479919796
DOIs	https://doi.org/10.1109/IAEAC.2015.7428612
State	Published - 7 Mar 2016
Event	IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 - Chongqing, China Duration: 19 Dec 2015 → 20 Dec 2015

Publication series

Name	Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

Conference

Conference	IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015
Country/Territory	China
City	Chongqing
Period	19/12/15 → 20/12/15

Keywords

adaptive Kalman filter
candidate data selection
second order mutual difference estimation
virtual redundant measurement
α-β-γ filter

Access to Document

10.1109/IAEAC.2015.7428612

Cite this

Zhang, Y., & Zhang, H. (2016). Adaptive Kalman filter based on improved second order mutual difference estimation. In B. Xu (Ed.), Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 (pp. 542-546). Article 7428612 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IAEAC.2015.7428612

Zhang, Yixin ; Zhang, Hai. / Adaptive Kalman filter based on improved second order mutual difference estimation. Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. editor / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 542-546 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015).

@inproceedings{812d90ff6d2945a897670c7687d0a0e3,

title = "Adaptive Kalman filter based on improved second order mutual difference estimation",

abstract = "In this paper, a method to compute noise variance and adapt measurement noise covariance matrix R in Kalman filter is proposed. We construct a virtual redundant measurement using α-β-γ filter to apply the second order mutual difference estimation method, which estimate noise variance effectively, in single measurement to calculate noise variance. And statistical data selection algorithm is proposed to avoid inaccuracy caused by lag in the α-β-γ filter. Simulations indicate this method is effective in R adaption with relatively low computation.",

keywords = "adaptive Kalman filter, candidate data selection, second order mutual difference estimation, virtual redundant measurement, α-β-γ filter",

author = "Yixin Zhang and Hai Zhang",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015 ; Conference date: 19-12-2015 Through 20-12-2015",

year = "2016",

month = mar,

day = "7",

doi = "10.1109/IAEAC.2015.7428612",

language = "英语",

series = "Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015",

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

pages = "542--546",

editor = "Bing Xu",

booktitle = "Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015",

address = "美国",

}

Zhang, Y & Zhang, H 2016, Adaptive Kalman filter based on improved second order mutual difference estimation. in B Xu (ed.), Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015., 7428612, Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015, Institute of Electrical and Electronics Engineers Inc., pp. 542-546, IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015, Chongqing, China, 19/12/15. https://doi.org/10.1109/IAEAC.2015.7428612

Adaptive Kalman filter based on improved second order mutual difference estimation. / Zhang, Yixin; Zhang, Hai.
Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. ed. / Bing Xu. Institute of Electrical and Electronics Engineers Inc., 2016. p. 542-546 7428612 (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015).

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

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AU - Zhang, Hai

PY - 2016/3/7

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N2 - In this paper, a method to compute noise variance and adapt measurement noise covariance matrix R in Kalman filter is proposed. We construct a virtual redundant measurement using α-β-γ filter to apply the second order mutual difference estimation method, which estimate noise variance effectively, in single measurement to calculate noise variance. And statistical data selection algorithm is proposed to avoid inaccuracy caused by lag in the α-β-γ filter. Simulations indicate this method is effective in R adaption with relatively low computation.

AB - In this paper, a method to compute noise variance and adapt measurement noise covariance matrix R in Kalman filter is proposed. We construct a virtual redundant measurement using α-β-γ filter to apply the second order mutual difference estimation method, which estimate noise variance effectively, in single measurement to calculate noise variance. And statistical data selection algorithm is proposed to avoid inaccuracy caused by lag in the α-β-γ filter. Simulations indicate this method is effective in R adaption with relatively low computation.

KW - adaptive Kalman filter

KW - candidate data selection

KW - second order mutual difference estimation

KW - virtual redundant measurement

KW - α-β-γ filter

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DO - 10.1109/IAEAC.2015.7428612

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BT - Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015

A2 - Xu, Bing

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 19 December 2015 through 20 December 2015

ER -

Zhang Y, Zhang H. Adaptive Kalman filter based on improved second order mutual difference estimation. In Xu B, editor, Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015. Institute of Electrical and Electronics Engineers Inc. 2016. p. 542-546. 7428612. (Proceedings of 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2015). doi: 10.1109/IAEAC.2015.7428612

Adaptive Kalman filter based on improved second order mutual difference estimation

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