Minimax filtering in linear stochastic uncertain discrete-continuous systems

Filtering of the states of a system, whose dynamics is defined by an Ito stochastic differential equation, by discrete and discrete-continuous observations is studied under the assumption that the intensities of continuous noises and covariance matrices of discrete noises are known only within to membership of certain uncertainty sets. A minimax approach is used to solve the problem. The filter is optimized with an integral quality criterion. Minimax filtering equations are derived from the solution of the dual optimization problem. A numerical solution algorithm for the problem is designed. Results of numerical experiments are presented.