An AR spectral analysis of non-stationary signals

Abstract A method of estimating an adaptive spectral density is described. At each time-sample of the signal an autoregressive (AR) filter is calculated by a covariance method. The length of the memory is adapted to the stationarity of the signal by a minimum prediction error power criterion. Advantages of a pole trajectories representation in the time frequency domain are illustrated. The evolution of the prediction error power and of the length of the memory versus time gives information about the stationarity of the signal. Resolution and precision obtained by this AR method are better than those obtained by a Fourier Transform method.

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