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.

翻译：考虑部分观测的线性随机微分方程模型，其依赖若干未知参数。本文提出对未观测分量的近似方法，该近似分三步实现：首先，通过矩估计方法构建未知参数的估计量；其次，利用该估计量定义一步极大似然估计过程；最后，将所获估计量代入卡尔曼-布西滤波器方程中，所得方程的解即为近似（自适应卡尔曼-布西滤波器）。本文描述了上述所有估计量及未知参数的极大似然估计与贝叶斯估计的渐近性质，并证明了所提自适应滤波器的渐近有效性。