This paper is also concerned with the problem of filtering under bounded nonrandom disturbances. We consider only stationary problems, i.e., all the parameters of the model are time-independent. Moreover, we search for a state estimate whose error is guaranteed to lie in a uniform ellipsoid (invariant ellipsoid) for all times; i.e., the estimate is uniform. The filter itself is also sought in the class of linear stationary filters. In this narrowed class of problems and estimates, the problem is totally solvable, i.e., an optimal filter and a state estimate can be constructed. It is this point that differs our setting from those mentioned above. The latter considered more general models, but the resulting solutions were only suboptimal, and no uniform estimates were obtained. Technically, we follow the linear matrix inequality approach [9, 10], which proved to be an effective tool in the analysis and synthesis of control systems but was little used in filtering problems. An exception is [11], in contrast to which we (i) give simpler and more accurate estimates for the quality of filtering, (ii) extend them to the discrete case, and (iii) analyze the behavior of estimates for large initial deviations. An important new technical tool is the S -theorem for two constraints [12], while the same theorem for a single constraint was applied previously.