Abstract
The autoregressive (AR) process is a general and widely used method for spectral estimation. Several papers have been dedicated to show the usefulness of AR process and its advantages. However, despite the fact that the spectrum obtained by AR estimator dependent on the choice of AR process order, the authors do not present an objective method for determination of this parameter. In this paper we show the importance of a proper choice of the process order and the superiority of the AR process over the FFT when one is dealing with drastically truncated data sets. Finally we use the procedure based on final prediction error criterion to determine the best AR process order for experimental data in a 2D NQR experiment.
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