An information rich subspace separation for non-stationary signal classification

This paper proposes a novel automated approach for the classification of highly non-stationary signals based on a non-principal component analysis (non-PCA) methodology. This method generates an eigen analysis based pseudospectrum to emulate the spectral characteristic variations of the non-stationary signals to be classified. Then, the estimated pseudo-spectrum is used to implement a comb like subspace filter structure, which captures the variations of all significant spectral components throughout the whole observation period. It is shown that this filter implementation method yields better results than the existing dimensionality reduction methods, which only utilise the principal k components of the eigen space. Finally, a novel probabilistic approach which creates a signature vector representing each class of signals in the training phase is proposed for the classification process. It is also shown that the proposed method can be effectively used not only for classification but also for the extraction of hidden stationary signature features from a non-stationary signal. Further, it is also proven that the proposed subspace filtering scheme can be used as a dynamic spectral estimation technique, which can eliminate the time frequency resolution tradeoff that exists in techniques such as short-time fourier transform (STFT).