Switching criterion for sub-and super-Gaussian additive noise in adaptive filtering

Abstract

Abstract Additive noise distributions can be divided into three types: Gaussian, super- and sub-Gaussian. The existing algorithms for adaptive filtering do not provide a better performance than the least mean square (LMS) method for the super- and sub-Gaussian noise simultaneously. For example, the maximum correntropy criterion performs better (worse) than the LMS method for super-Gaussian (sub-Gaussian) noise, whereas the least mean fourth performs better (worse) than the LMS method for sub-Gaussian (super-Gaussian) noise. We propose a criterion for switching between sub- and super- Gaussian additive noise, that could be used to assess whether the error signal had a sub- or super-Gaussian profile, and thus determine which algorithm would work best in the iterative process. Simulations demonstrate that the switching criterion helps the proposed switching algorithm to produce a better performance than the LMS algorithm for sub and super-Gaussian noise simultaneously.