  The model of partially observed linear stochastic differential equations
depending on some unknown parameters is considered. An proximation of the
unobserved component is proposed. This approximation is realized in three
steps. First an estimator of the thod of moments of unknown parameter is
constructed. Then this estimator is used for defining the One-step MLE-process
and nally the last estimator is substituted to the equations of Kalman-Bucy
(K-B) filter. The solution of obtained K-B equations ovide us the approximation
(adaptive K-B filter). The asymptotic properties of all mentioned estimators
and MLE and Bayesian timators of the unknown parameters are described. The
asymptotic efficiency of the proposed adaptive filter is shown.
