Decomposition of problems in the estimation of the state of multidimensional stochastic systems

A method is proposed for decomposing problems of estimating the state of nonlinear systems described by stochastic differential Ito conditions. The method leads to an approximate nonlinear filtering algorithm for estimating the state vector of a multidimensional system subjected to random signals. The computer load in determining the filter coefficients is lower and the time for their estimation is much shorter than in conventional methods. Nonlinear filters obtained through decomposition of the initial estimation problem are provided.