Step-like signal processing with distinct finite number of levels

An algorithm for filtering noisy step-like signals is proposed. This algorithm is based on the assumption of Gaussian contamination. In this procedure data within a moving window is divided into two almost equal clusters and a hypothesis tests (F-test) for differences in the means between two such clusters. Histograms analysis and/or our a priori knowledge about the number of discrete amplitudes in the ideal noise free signal provide information that is used to filter the signal further and produce a clean signal with the desired number of discrete amplitudes (levels). As an illustration the method is tested by simulation