Fast Extraction for Skewed Source Signals Using Conditional Expectation

Abstract

The extraction of the stochastic source signals whose probability density functions (PDFs) are skewed is very important in many applications such as biomedical signal processing and mechanical fault diagnosis. This paper shows that the skewed source signal with the maximal absolute value of skewness can be fast extracted by a proposed algorithm using conditional expectation. Compared with the existing conditional expectation-based algorithms, the proposed one possesses two main advantages. One is that it does not require the prior knowledge of the positive support of the desired source, namely the time indices where the source of interest is positive. The other is that it can be employed both in the determined and underdetermined cases. Furthermore, the proposed algorithm is mainly based on the first- and second-order statistics and does not need the preprocessing so that the computational cost is significantly low. Simulation results show the superiority of the proposed algorithm over the existing methods and indicate that the proposed algorithm also performs well in the underdetermined case when the number of sensors is slightly less than that of sources.