Data-driven method for Kalman filtering

Wen Xie; Yuanqing Xia

doi:10.1109/ICICIP.2011.6008364

Data-driven method for Kalman filtering

Wen Xie^*, Yuanqing Xia

^*Corresponding author for this work

School of Automation

Beijing Institute of Technology

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

2 Citations (Scopus)

Abstract

In this paper, the state estimation problem is considered based on the input-output data. A data-driven subspace identification method combined with the Kalman on-line filtering algorithm is proposed for solving the state estimation problem for a class of dynamical systems where the exact models can not be established. Simulation results are further presented to show the effectiveness of the proposed strategy.

Original language	English
Title of host publication	Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
Pages	830-835
Number of pages	6
Edition	PART 2
DOIs	https://doi.org/10.1109/ICICIP.2011.6008364
Publication status	Published - 2011
Event	2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011 - Harbin, China Duration: 25 Jul 2011 → 28 Jul 2011

Publication series

Name	Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
Number	PART 2

Conference

Conference	2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011
Country/Territory	China
City	Harbin
Period	25/07/11 → 28/07/11

Access to Document

10.1109/ICICIP.2011.6008364

Cite this

Xie, W., & Xia, Y. (2011). Data-driven method for Kalman filtering. In Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011 (PART 2 ed., pp. 830-835). Article 6008364 (Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011; No. PART 2). https://doi.org/10.1109/ICICIP.2011.6008364

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title = "Data-driven method for Kalman filtering",

abstract = "In this paper, the state estimation problem is considered based on the input-output data. A data-driven subspace identification method combined with the Kalman on-line filtering algorithm is proposed for solving the state estimation problem for a class of dynamical systems where the exact models can not be established. Simulation results are further presented to show the effectiveness of the proposed strategy.",

author = "Wen Xie and Yuanqing Xia",

year = "2011",

doi = "10.1109/ICICIP.2011.6008364",

language = "English",

isbn = "9781457708145",

series = "Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011",

number = "PART 2",

pages = "830--835",

booktitle = "Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011",

edition = "PART 2",

note = "2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011 ; Conference date: 25-07-2011 Through 28-07-2011",

}

Xie, W & Xia, Y 2011, Data-driven method for Kalman filtering. in Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011. PART 2 edn, 6008364, Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011, no. PART 2, pp. 830-835, 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011, Harbin, China, 25/07/11. https://doi.org/10.1109/ICICIP.2011.6008364

Data-driven method for Kalman filtering. / Xie, Wen; Xia, Yuanqing.
Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011. PART 2. ed. 2011. p. 830-835 6008364 (Proceedings of the 2nd International Conference on Intelligent Control and Information Processing, ICICIP 2011; No. PART 2).

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

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AB - In this paper, the state estimation problem is considered based on the input-output data. A data-driven subspace identification method combined with the Kalman on-line filtering algorithm is proposed for solving the state estimation problem for a class of dynamical systems where the exact models can not be established. Simulation results are further presented to show the effectiveness of the proposed strategy.

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DO - 10.1109/ICICIP.2011.6008364

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Data-driven method for Kalman filtering

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