Quality assessment of hybrid nonlinear filters

Abstract

Traditionally, research on adaptive signal processing has been conducted with the aim of designing adaptive filters with high performance in terms of some prescribed performance measure. However, little is known about how such filters influence the nature of the processed signal. Based upon some recently introduced results in dealing with nonlinearity within a signal in hand, we provide a critical assessment of the qualitative performance of common linear and nonlinear filters and their combinations. An insight into the performance of so called hybrid filters is provided, which is achieved for combinations of standard nonlinear (neural) and linear filters. It is shown that depending on the application, it is important not only to look for best filter performance in terms of some quantitative measure of the error but also for a filter that will not change the character of a signal. Simulation results support the analysis.