BLIND SEPARATION OF MIXED-KURTOSIS SIGNALS USING AN ADAPTIVE THRESHOLD NONLINEARITY
H Mathis, Thomas P. von Hoff, M. Joho · 2000
A parameterized threshold nonlinearity, which separates a mixture of signals with any distribution (except for Gaussian), is introduced. This nonlinearity is particularly simple to implement, since it neither uses hyperbolic nor polynomial functions, unlike most nonlinearities used for blind separation. For some specific distributions, the stable region of the threshold parameter is derived, and optimal values for best separation performance are given. If the threshold parameter is made adaptive during the separation process, the successful separation of signals whose distribution is unknown is demonstrated and compared against other known methods. 1. INTRODUCTION Blind signal separation using higher-order statistics either explicitly or implicitly has attracted many researchers whose main goal is to separate a set of mixed signals as fast as possible with the smallest residual mixing. Most approaches require complete or at least some knowledge of the source distributions. If sources ...