Abstract
This section is devoted to approaches for the formation, estimation and application of diagnostic parameters, as well as stochastic models in monitoring problems. The procedures for increasing the reliability and depth of dynamic systems monitoring are substantiated based on the decomposition of diagnostic models, the use of combined statistical criteria and the processing of observations in “forward” and “backward” time using the extended Kalman filter. The possibility of detecting and parrying anomalous observations using robust filtering procedures is shown. Such procedures are based on the use of an influence function that establishes the level of confidence for formed observations. U-D modification of the Kalman filter with an influence function in the observation selection loop is presented. Here U is an upper triangular matrix with unit diagonal elements and D is a diagonal matrix. Results of the mathematical simulation are given.