26 June 2017 Interferometric signals analysis based on the extended Kalman filter tuned by machine learning technique
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Abstract
The paper deals with the machine learning approach to automatic tuning of extended Kalman filter in application to interferometric signals processing. The representation of interferometric signals as output of dynamic systems with varying state vector is presented. It is shown that the challenge of the extended Kalman filter application to interferometric data processing is selection of initial parameters for the filter. The complex tuning problem is described in a formal form. The machine learning approach to the automatic filter tuning is proposed. The combination of Monte Carlo optimization and the gradient descent are implemented for initial filter parameters selection. The optimization criterion in the form of sum differences between measured and estimated signal value is presented and discussed. The results of simulated and experimental interferometric signals processing are presented and analyzed. The quality of amplitude and phase estimation by the automatically tuned filter is at the same level as hand tuned filter. It is shown, that proposed approach allows to obtain robust results of experimental data processing.
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Petr A. Ermolaev, Maxim A. Volynsky, "Interferometric signals analysis based on the extended Kalman filter tuned by machine learning technique", Proc. SPIE 10329, Optical Measurement Systems for Industrial Inspection X, 103293D (26 June 2017); doi: 10.1117/12.2269653; https://doi.org/10.1117/12.2269653
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