A COMPARISON STUDY BETWEEN RECURSIVE INVERSE AND ITERATIVE WIENER FILTER ALGORITHMS

The recently proposed recursive inverse ( RI) and iterative Wiener filter (IWF) algorithms both have performances comparable to the recursive-least-squares (RLS) algorithm. However, the RI and IWF are better than the RLS algorithm in terms of computational complexity, tracking ability and stability. Even though, the RI and IWF algorithms have the same structure, but the RI algorithm is better in many aspects. In this paper, we introduce a comparison study between the RI and IWF algorithms. Also, we compare their performances with the RLS algorithm for system identification and linear prediction settings in additive white and correlated, Gaussian and impulsive, noise environments.