Autoregressive modeling of the Wigner spectrum

Autoregressive(AR) or linear predictive(LP) modeling and Wigner time-frequency representations have been proposed for non-stationary signal analysis and synthesis, owing to their specific advantages over the short-time Fourier transform, viz. reduced data set characterisation and improved frequency resolution of the former, and the improved time resolution and thence better non-stationary signal representation of the latter. However, the former is limited in time resolution and the latter in frequency resolution and size of characterising data set, depending on the size of the windows that need to be used. This paper investigates the potential combination of the two above methods, with the aim of exploiting their advantages simultaneously and addressing the window size-resolution dilemma. The concept shows good promise to this end, despite the problems of the cross-spectral components and computational complexity, that need to be addressed. Examples of simulated signals are presented to illustrate the advantages of this representation.