Methods for estimating the autocorrelation and power spectral density functions when there are many missing data values

Author

Grossbard, Neil ; Dewan, Edmond

Author_Institution

Inst. for Space Res., Boston Coll., MA, USA

fYear

1990

fDate

10-12 Oct. 1990

Firstpage

30

Lastpage

34

Abstract

A new method for estimating the autocorrelation and the crosscorrelation has been developed. The resulting estimates are usually more accurate than the classical values. The method is particularly useful when there are many missing data values. For the case when there are many missing data values, it is suggested that a power spectral density (PSD) of the autocorrelation function can be developed. The resulting PSD can easily be mapped into the PSD of the original data. Towards this end, Burg´s technique has been applied to the autocorrelation and the results of the application are presented.<>

Keywords

correlation methods; spectral analysis; Burg´s technique; autocorrelation; correlation estimation; crosscorrelation; many missing data values; mapped PSD; original data; power spectral density functions; signal processing; spectral analysis; Autocorrelation; Educational institutions; Frequency measurement; Geophysics; H infinity control; Laboratories; Phase estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Spectrum Estimation and Modeling, 1990., Fifth ASSP Workshop on

Conference_Location

Rochester, NY, USA

Type

conf

DOI

10.1109/SPECT.1990.205540

Filename

205540