Adaptive system identification based on higher-order statistics
Document typeConference report
Rights accessOpen Access
The problem of estimating the autoregressive (AR) parameters of a causal AR moving average (ARMA) (p,q) process using higher-order statistic is addressed. It is shown that there is always a linear combination of p+1 slices that gives a full-rank Toeplitz matrix. This derivation proves that consistent estimates can always be obtained with this set of p+1, 1-D slices. These results lead to the development of a new adaptive lattice algorithm with improved performance. Some results are presented comparing this scheme with previous algorithms based on a single slice. Estimation of the MA parameters of the obtained AR-compensated sequence completes the identification of the system. As this method is based on cumulants, the estimation will be unbiased, even in the presence of colored Gaussian noise
CitationFonollosa, José A. R., Masgrau, E. Adaptive system identification based on higher-order statistics. A: IEEE International Conference on Acoustics, Speech, and Signal Processing. "International Conference on Acoustics, Speech and Signal Processing 1991". Toronto, Ontario: 1991, p. 3437-3440.