Signal recognition: Both components of the short time Fourier transform vs. power spectral density

Abstract

A new feature representation approach was generalised and used for Gaussian recognition. The generalised approach consists of simultaneously using two new recognition features — real and imaginary Fourier components —taking into account the covariance between features. Generalisation of the approach improves recognition effectiveness. An advanced time-frequency technique, the short time Fourier transform, was considered. Covariance and the correlation coefficient between the proposed features were obtained for the first time for arbitrary stationary signals. The recognition effectiveness between the generalised approach and power spectral density was compared. It was shown that power spectral density is not an optimal feature, and represents only a particular case of the generalised approach. The use of power spectral density is optimal if simultaneously the correlation coefficient between Fourier components is equal to zero, and the standard deviations of components are equal. Use of the generalised approach provides an increase in effectiveness in comparison with power spectral density.