Single-Channel Blind Source Separation for Periodic Electromagnetic Switching Noise

In this article, a single-channel blind source separation method is proposed to decompose time-domain measurement results of electromagnetic noise into the underlying periodic switching noise source signals without requiring any information about the sources. This is realized by clustering the time-domain waveforms based on their period and similarity. At first, the start and end times of the waveforms need to be defined. Then, periods are determined and a waveform cluster is assigned to each period using a new probabilistic approach. Only in the final step, the waveform shape is considered, when the clusters are filtered by removing the waveform outliers that are too different from the remaining waveforms of each cluster. To this purpose, a distance definition based on the cross correlation among the waveforms is proposed. Using numerical and measurement examples, it is shown that it is possible to determine and rank the contribution to the spectrum of each source separately. This helps individuating the main noise sources more quickly and to analyze the contribution of single sources in a complex noisy environment.