Filtering and analysis of linear discrete control systems under a nonparametric specification of the correlated noises

Algorithms of conditionally optimal filtering and analysis in closed-loop linear control systems with correlated noise are synthesized from nonparametric description of truncated sampled-data covariance functions, the state space needing no expansion. The algorithms are compared in terms of computing effectiveness.