Filtering of nonstationary stochastic processes with bounded dispersions of derivatives

The feasibility of guaranteeing the accuracy at filtering with substantial prior uncertainty in a class of input signals is analyzed. The class y₂ containing nonstationary stochastic signals with the only known prior information - upper bounds of some derivatives is considered. The procedure of estimating the extreme attainable error dispersion at filtering the input signals of the class y₂ at the noise with known correlation function is given. The proposed methods are applicable in automatic control and system analysis.