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

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 corren-tropy 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. (C) 2018 Elsevier B.V. All rights reserved.