Suppression and detection of impulse type interference using adaptive median hybrid filters

Abstract

In this paper, we introduce a new type of nonlinear filters, the Adaptive Median Hybrid (AMH) filters, for the suppression and detection of short duration interferences. In the AMH filters, adaptive filter substructures are used to estimate the current signal value from the future and past signal values. The output of the overall filter is the median of the adaptive filter outputs and the current signal value. This kind of nonlinear filter structure is shown to adapt and preserve rapid changes in signal characteristics well. However, it filters out short duration interferences. By examining the difference between the original and filtered data, interferences can be detected. We introduce two types of AMH filters, the AMH filter with separate adaptive substructures (SAMH) and the AMH filter with coupled substructures (CAMH), which have different convergence properties and implementation. We use both synthetic and real data (speech and electroencephalogram (EEG)) to show the applicability of the proposed filters.