Comparison of three auto-regressive modeling methods

There are many approaches to the computation of the auto-regressive model of a signal. To gain familarity with the tradeoffs of various methods, the performances of three popular algorithms are examined in this study. The performances of the covariance (COV), the singular-value-decomposition (SVD), and the pencil-of-functions (POF) methods are compared. The accuracy of the pole locations for the modeled signal is measured as a function of the signal-to-noise ratio (SNR) and the record length. The results of this study indicate that for an SNR below 50 db, both the SVD and the POF are superior to the COV method. In terms of error only, the SVD is either comparable or slightly superior to POF. However POF, with noise-correction incorporated, may prove to be comparable to SVD.