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

fYear

1990

fDate

3-6 Apr 1990

Firstpage

1595

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1990. ICASSP-90., 1990 International Conference on

Conference_Location

Albuquerque, NM

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.1990.115727

Filename

115727

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2890674