Title :
A class of fast covariance least squares algorithms
Author :
Todd, Richard M. ; Cruz, J.R.
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Oklahoma Univ., Norman, OK, USA
Abstract :
A new class of fast least squares linear prediction algorithms for frequency estimation is described, and a specific example of this class of algorithms is presented in detail. A simulation for the case of single-sinusoid inputs shows that the estimates are quite sensitive to noise for frequencies near half the Nyquist frequency. A simplified error analysis seems to confirm the existence of the noise sensitivity in that range of frequencies
Keywords :
filtering and prediction theory; least squares approximations; spectral analysis; Nyquist frequency; error analysis; fast covariance least squares algorithms; frequency estimation; linear prediction algorithms; noise sensitivity; simulation; single-sinusoid inputs; Computational modeling; Computer science; Error analysis; Frequency estimation; Laboratories; Least squares approximation; Least squares methods; Polynomials; Prediction algorithms; Signal processing algorithms;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on
Conference_Location :
Albuquerque, NM
DOI :
10.1109/ICASSP.1990.115727
