New methods for the synthesis of set theoretic estimates [digital signal processing]

Two methods for the synthesis of set theoretic estimates are presented. The first is a generalization of the method of successive projections onto closed and convex subsets of Hilbert spaces to approximately compact subsets of metric spaces. The second is based on a stochastic search in the solution space. These methods allow greater flexibility with regard to the incorporation of prior knowledge, thereby extending the scope of set theoretic estimation. Applications to digital signal processing problems are discussed