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Proceedings Paper

Kalman filter for noise removal in optical fiber sensing system
Author(s): Tao Liu; Wen-ping Zhang; Hui-fang Chen; Gui-Lan Feng
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Paper Abstract

Noise in the optical fiber sensing system, produced in laser, transmission, demodulation and environment, reduces the Signal-to-Noise Ratio (SNR) and the measuring accuracy of the whole system . In this paper, a statistical approach based on Kalman-filter is undertaken to removal noise of the measured object real time, and then to improve the accuracy of the fiber sensing system. The temperature induced by fiber sensing is modeled as a discrete-time state variable by a Gauss-Markov random process with the Gaussian white and additive noise in the linear dynamic system. Based on Bayesian MAP Inference and minimum mean-square error criterion (MMSE), gain of the kalman-filter and the state error covariance can be regulated by Measurement Update equations to correct posteriori state estimate. Such recursive algorithm can finally get the optimum estimator of the state through time. The performances of the model and the algorithm are investigated in the DOFS temperature sensing system. Variance is used to evaluate its performance in noise removal. At the same time, the experimental results of the method proposed is compared with original measurement data analysis. The algorithm performs more improvement in accuracy of the fiber sensing system, and implements the real-time measurement.

Paper Details

Date Published: 15 October 2012
PDF: 6 pages
Proc. SPIE 8417, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 84171L (15 October 2012); doi: 10.1117/12.977936
Show Author Affiliations
Tao Liu, China Jiliang Univ. (China)
Wen-ping Zhang, Hangzhou OE Technology Co., Ltd. (China)
Hui-fang Chen, China Jiliang Univ. (China)
Gui-Lan Feng, China Jiliang Univ. (China)


Published in SPIE Proceedings Vol. 8417:
6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment
Yudong Zhang; Libin Xiang; Sandy To, Editor(s)

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