Active parametric identification of stochastic linear discrete system based on optimal design of input signals and initial states

Under consideration are some theoretical and applied aspects of the active parametric identification of the multidimensional stochastic linear discrete systems described by state space models. Original results are obtained in the case that the parameters of mathematical models to be estimated appear in various combinations in the state and measurement equations, as well as the covariance matrices of the process noise and measurement errors. By an example of one model structure, the efficiency and sensibility is shown of applying the active identification procedure based on optimal design of input signals and initial states.