Using nonlinear filtering for matching random process

The problem of matching noise samples of random processes is studied in the context of nonlinear filtering theory. The relation between the optimal matching algorithm and the similar algorithm derived in the case when one of the samples is exactly known is discussed. The Cramer-Rao inequality is used to analyze the matching accuracy. The reasons of matching accuracy degradation in comparison with the matching problem when one of the samples is known are investigated.