Abstract

This paper describes signals in optical coherent tomography using probability models and presents a comparative analysis of an extended Kalman filter and the sequential Monte Carlo method for dynamic estimation of the signal parameters. The results of a comparison of the estimation errors and the response rate of the algorithms are presented. It is shown that the quality of the images formed by means of an extended Kalman filter and the sequential Monte Carlo method depends on the available a priori information concerning the characteristics of the data to be processed. Recommendations are made of the use of signal-processing algorithms in correlation optical coherent tomography.

© 2015 Optical Society of America

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