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A new recursive algorithm for autoregressive (AR) spectral estimation is introduced, based on the least squares solution for the AR parameters using forward and backward linear prediction. The algorithm has computational complexity proportional to the process order squared, comparable to that of the popular Burg algorithm. The computational efficiency is obtained by exploiting the structure of the least squares normal matrix equation, which may be decomposed into products of Toeplitz matrices. AR spectra generated by the new algorithm have improved performance over AR spectra generated by the Burg algorithm. These improvements include less bias in the frequency estimate of spectral components, reduced variance in frequency estimates over an ensemble of spectra, and absence of observed spectral line splitting.
L. Marple (Fri,) studied this question.