Set theoretic estimation by random search

An adapted random search algorithm is shown to be a feasible method for the synthesis of set theoretic estimates. It circumvents the theoretical and computational shortcomings of existing deterministic methods and does not place any geometrical restrictions on the sets. The proposed method can handle arbitrarily complex property sets in applications for which the number of unknown parameters is typically low (e.g. parametric multidimensional spectral estimation, system identification, blur identification, and filter design)