Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications

Wenjun Lv; Yu Kang; Yunbo Zhao

doi:10.1109/JAS.2018.7511183

Volume 5 Issue 6

Nov. 2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2018 > 5(6): 1054-1061

Wenjun Lv, Yu Kang and Yunbo Zhao, "Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1054-1061, Nov. 2018. doi: 10.1109/JAS.2018.7511183

Citation:

Wenjun Lv, Yu Kang and Yunbo Zhao, "Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1054-1061, Nov. 2018. doi: 10.1109/JAS.2018.7511183

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Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications

doi: 10.1109/JAS.2018.7511183

Wenjun Lv^1
,,
Yu Kang^{2, 3
,
,},
Yunbo Zhao^4
,

1.
Department of Automation, University of Science and Technology of China, Hefei 230027, China
2.
State Key Laboratory of Fire Science, Department of Automation and Institute of Advanced Technology, University of Science and Technology of China, Hefei 230027, China
3.
Key Laboratory of Technology in GeoSpatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
4.
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China

Funds:

the National High Technology Research and Development Program of China (863 Program) 2014AA06A503

the National Natural Science Foundation of China 61422307

the National Natural Science Foundation of China 61673350

the National Natural Science Foundation of China 61673361

More Information

Author Bio:
Wenjun Lv is currently a Ph.D. candidate in control science and engineering at University of Science and Technology of China. His research interests include filtering theory and localization technology. (email: lvwenjun@mail.ustc.edu.cn)

Yu Kang (M'09-SM'14) received his Dr. Eng. degree in control theory and control engineering from University of Science and Technology of China, Hefei, China, in 2005. From 2005 to 2007, he was a Post-Doctoral Fellow with the Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China. He is currently a Professor with the State Key Laboratory of Fire Science, Department of Automation, and the Institute of Advanced Technology, University of Science and Technology of China, and with the Key Laboratory of Technology in GeoSpatial Information Processing and Application System, Chinese Academy of Sciences. His current research interests include monitoring of vehicle emissions, adaptive/robust control, variable structure control, mobile manipulator, and Markovian jump systems

Yunbo Zhao (M'10-SM'14) received the BSc degree in mathematics from Shandong University, China in 2003, MSc degree in systems sciences from the Key Laboratory of Systems and Control, Chinese Academy of Sciences, China in 2007, and Ph.D. degree in control engineering from the University of South Wales (formerly University of Glamorgan), Pontypridd, UK in 2008, respectively. He has held postdoctoral positions with INRIA Grenoble, France, University of Glasgow, UK and Imperial College London, UK, respectively. He is currently a Professor with Zhejiang University of Technology, China. His research interests include networked control systems and systems biology. (email: ybzhao@zjut.edu.cn)
Corresponding author: Yu Kang, email: kangduyu@ustc.edu.cn
Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.
Received Date: 2016-06-02
Accepted Date: 2016-10-26

Abstract

Abstract

In this paper, optimal filtering problem for a class of linear Gaussian systems is studied. The system states are updated at a fast uniform sampling rate and the measurements are sampled at a slow uniform sampling rate. The updating rate of system states is several times the sampling rate of measurements and the multiple is constant. To solve the problem, we will propose a self-tuning asynchronous filter whose contributions are twofold. First, the optimal filter at the sampling times when the measurements are available is derived in the linear minimum variance sense. Furthermore, considering the variation of noise statistics, a regulator is introduced to adjust the filtering coefficients adaptively. The case studies of wheeled robot navigation system and air quality evaluation system will show the effectiveness and practicability in engineering.
- Air quality evaluation system,
- linear Gaussian system,
- wheeled robot navigation system

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References(31)

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Self-Tuning Asynchronous Filter for Linear Gaussian System and Applications

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