The Kalman filter has been widely applied for state identification in controllable systems. As a special case of hidden Markov model, it is based on the assumption of linear dependency relationships and Gaussian noises. The classical Kalman filter does not differentiate systematic error from random error associated with observations. In this paper, we propose an extended Kalman filtering mechanism based on generalized interval probability, where systematic error is represented by intervals, state and observable variables are random intervals, and interval-valued Gaussian distributions model the noises. The prediction and update procedures in the new mechanism are derived. A case study of auto-body side frame assembly is used to illustrate the developed mechanism.
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ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
August 17–20, 2014
Buffalo, New York, USA
Conference Sponsors:
- Design Engineering Division
- Computers and Information in Engineering Division
ISBN:
978-0-7918-4628-5
PROCEEDINGS PAPER
A Kalman Filtering Mechanism Based on Generalized Interval Probability and its Application in Process Variation Estimation
Yan Wang,
Yan Wang
Georgia Institute of Technology, Atlanta, GA
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Aiguo Cheng,
Aiguo Cheng
Hunan University, Changsha, Hunan, China
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Zhihua Zhong
Zhihua Zhong
Hunan University, Changsha, Hunan, China
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Jie Hu
Hunan University, Changsha, Hunan, China
Yan Wang
Georgia Institute of Technology, Atlanta, GA
Aiguo Cheng
Hunan University, Changsha, Hunan, China
Zhihua Zhong
Hunan University, Changsha, Hunan, China
Paper No:
DETC2014-34543, V01AT02A040; 12 pages
Published Online:
January 13, 2015
Citation
Hu, J, Wang, Y, Cheng, A, & Zhong, Z. "A Kalman Filtering Mechanism Based on Generalized Interval Probability and its Application in Process Variation Estimation." Proceedings of the ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1A: 34th Computers and Information in Engineering Conference. Buffalo, New York, USA. August 17–20, 2014. V01AT02A040. ASME. https://doi.org/10.1115/DETC2014-34543
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