Abstract
The fuzzy-set theory is used to synthesize an optimum linear meter for observations in the presence of correlated interferences. It is shown that replacement of the linear discriminator in the classical Kalman filter with a nonlinear discriminator based on calculation of fuzzy preference relations allows obtaining the optimum solution for observations performed in the presence of both correlated and uncorrelated interferences. The operability and efficiency of the synthesized meter are confirmed by the results of comparative simulation. The area of application of the synthesized meter comprises data processing systems with the dominating requirement of simple implementation.
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