Construction an observation in the Shestakov–Sviridyuk model in terms of multidimensional “white noise” distortion

The Shestakov–Sviridyuk model is a mathematical model of a measuring unit used to reconstruct a dynamically distorted signal with the help of experimental data. This model is also called the optimal dynamic measurement problem. The theory of optimal dynamic measurement is based on the problem of minimizing the difference between the values of a virtual observation obtained using a computational model and experimental data, usually distorted by some disturbances. The article describes the Shestakov–Sviridyuk model of optimal dynamic measurement in terms of various types of disturbances. It focuses on the preliminary stage of the study of the optimal dynamic measurement problem, namely, the Pyt'ev–Chulichkov method for constructing observation data, i. e. converting experimental data to clean them from disturbances in the form of “white noise”, which is understood as the Nelson–Glicklich derivative of the multidimensional Wiener process. To use this method, a priori information on the properties of the functions describing the observation, is used.