Anisotropy-based suboptimal filtering for the linear discrete time-invariant systems

Consideration was given to the problem of robust filtering for the finite-dimensional linear discrete time-invariant system with measured and estimated outputs. The system is exposed to a random disturbance with the imprecisely known probability distribution. In the information-theoretical terms, the stochastic uncertainty of the input disturbance is defined by the functional of mean anisotropy. The error of estimation was quantified by the anisotropy norm. A sufficient condition for an estimator to exist and ensure that the error is less than the given threshold value was derived in the form of a convex inequality on the determinant of a positive definite matrix and two linear matrix inequalities.