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СOMPUTATIONAL COMPLEXITY ANALYSIS OF RECURRENT DATA PROCESSING ALGORITHMS IN OPTICAL COHERENCE TOMOGRAPHY

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The paper deals with the basic principles of signals representation in optical coherence tomography with the usage of dynamic systems theory formalism. Computational complexity of algorithms for dynamic estimation of signals parameters is analyzed, such as extended Kalman filter and sequential Monte-Carlo method. It is shown that processing time of one discrete-time sample of the signal by extended Kalman filter increases polynomially with sizes of parameters vector and observation vector. Processing time of one discrete-time sample of the signal by sequential Monte-Carlo method depends linearly both on sizes of parameters vector and observation vector, and on the number of generating random vectors. Experimental results of processing time measurement by each algorithm are described. It is shown that processing time of the signal containing 500 discrete-time samples by extended Kalman filter in the case of the simplest model is approximately equal to 0.1 seconds and increases several times with complication of the model. Processing time of the same signal by sequential Monte-Carlo methods with fixed number of generated random vectors is equal to 0.7 seconds and slightly increases with complication of the model, approximately by 1.5 times. Obtained results may be used for estimation of expected data processing time by recurrent dynamic estimation algorithms in optical coherence tomography systems.

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