Kalman filter for noise removal in optical fiber sensing system

Tao Liu; Wen-ping Zhang; Hui-fang Chen; Gui-Lan Feng

doi:10.1117/12.977936

15 October 2012 Kalman filter for noise removal in optical fiber sensing system

Tao Liu, Wen-ping Zhang, Hui-fang Chen, Gui-Lan Feng

Proceedings Volume 8417, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment; 84171L (2012) https://doi.org/10.1117/12.977936
Event: 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies (AOMATT 2012), 2012, Xiamen, China

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.

Citation Download Citation

Tao Liu, Wen-ping Zhang, Hui-fang Chen, and Gui-Lan Feng "Kalman filter for noise removal in optical fiber sensing system", Proc. SPIE 8417, 6th International Symposium on Advanced Optical Manufacturing and Testing Technologies: Optical Test and Measurement Technology and Equipment, 84171L (15 October 2012); https://doi.org/10.1117/12.977936

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KEYWORDS

Sensing systems

Signal to noise ratio

Interference (communication)

Filtering (signal processing)

Temperature metrology

Optical fibers

Denoising

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