Title

Recursive Maximum Likelihood Estimation of Autoregressive Processes

Document Type

Article

Date of Original Version

1-1-1983

Abstract

A new method of autoregressive parameter estimation is presented. The technique is a closer approximation to the true maximum likelihood estimator than that obtained using linear prediction techniques. The advantage of the new algorithm is mainly for short data records and/or sharply peaked spectra. Simulation results indicate that the parameter bias as well as the variance is reduced over the Yule-Walker and the forward-backward approaches of linear prediction. Also, spectral estimates exhibit more resolution and less spurious peaks. A stable all-pole filter estimate is guaranteed. The algorithm operates in a recursive model order fashion, which allows one to successively fit higher order models to the data. © 1983 IEEE

Publication Title

IEEE Transactions on Acoustics, Speech, and Signal Processing

Volume

31

Issue

1

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