Spectrum Separation for Electromagnetic Noise using Eigen-Value Decomposition of Correlation Matrix
Hiroshi Hirayama, Nobuyoshi Kikuma, Kunio Sakakibara · 2005
It is required to reduce undesired emissions from electric equipment. Spectrum measurement is the first step to reduce the undesired emissions. We propose a method to separate spectra based on the statistical independence of sources. Waveforms received are sampled by a digitizing oscilloscope. After applying FFT to the obtained waveforms, a correlation matrix is calculated. Finally, by applying eigenvalue decomposition to the correlation matrix, separated spectra are obtained. The independence of sources is used as a priori knowledge to separate the spectra. The validity of the proposed method was demonstrated experimentally.