Separation of superimposed signals by a cross-correlation method

A time-domain approach is presented for separating a superposition of signals which overlap both in the time and frequency domains. The method is partly based on the cross-correlations among the signals and some of their derivatives. Given the data, an initial detection scheme formulates a set of hypotheses on the sets of signals which may be present in the mixture, and provides estimates of the signal positions for each hypothesis. In the second stage, the most likely hypothesis is selected and accurate values of the signal amplitudes and their time positions are determined by a nonlinear least squares approach. The method is illustrated by a simulation with emg signals.