The paper emphasizes the importance of normalization of parameters in identification of linear models as now commonly applied to digital signal processing. Classical LPC, Pisarenko, Prony methods are unified and compared. The factorial approach plays a central role when additive noise is considered. The computational requirement is the determination of eigen vectors of correlation and covariance matrices. Various algorithms are then given including sequential estimation procedures in the covariance case. The methods are compared on close sinewaves merged in noise in terms of resolution, windowing, signal-to-noise ratio.
['C. Gueguen', 'Yves Grenier', 'F. Giannella']
Factorial linear modelling, algorithms and applications