Experimental comparison of three multichannel linear prediction spectral estimators

Single-channel spectral estimators based on linear prediction techniques, such as the maximum-entropy method, have been shown to often provide better spectral stability and resolution than standard FFT procedures for short data sequences. Based on this improved performance, a multitude of multichannel linear prediction techniques have been promoted for processing multichannel data sequences. Three of these are examined in the paper: a multichannel generalisation of the single-channel Burg algorithm by Nuttall, a maximum-entropy type of algorithm by Morf, Vieira, Lee and Kailath, and a multichannel extension of the covariance method of linear prediction implemented by Marple. For purposes of experimental comparison, various two-channel data sets were processed by the three methods to produce the two autospectra, the magnitude-squared coherence and the coherence phase associated with each data set. A possible deleterious effect of signal `feed-across¿¿ between autospectra and in the coherence has been discovered in all three methods. The phenomenon, due to inexact pole-zero cancellation, is especially prominent for short data sequences. Based on the multichannel results given here, the Nuttall method generally produced the best spectral estimates.