A nonlinear digital filter using fuzzy clustering

A novel digital signal processing technique, fuzzy filtering, is proposed for estimating signals with edges, contaminated with additive white Gaussian noise. In this filter, the concept of fuzzy clustering is utilized for classifying signals. This filter classifies the input signal sequence into a flat part and a changing one ambiguously using a fuzzy-logic membership function. Then, the signal is estimated on the basis of the classification. Fuzzy clustering is more effective than conventional definite classification, since some edges in the signal are ambiguous. Moreover, by combining with median filtering a filter for removing both white Gaussian noise and impulsive noise can be obtained. Computer simulations demonstrate its superior capability