DocumentCode
2337536
Title
AR spectral estimation of segmented time signals
Author
Zhou, Ping ; Poularikas, A.D.
Author_Institution
Dept. of Electr. & Comput. Eng., Alabama Univ., Huntsville, AL, USA
fYear
1990
fDate
11-13 Mar 1990
Firstpage
536
Lastpage
539
Abstract
The problem of estimating the power spectral density of stationary time series when the measurements are not contiguous is discussed. Two autoregressive (AR) modeling methods are proposed to handle are spectral estimation of the segmented time signals and show the statistical efficiency in numerical examples. The performance of the proposed AR method is illustrated by simulation examples. The examples indicate that the method performs satisfactorily
Keywords
spectral analysis; statistical analysis; time series; AR spectral estimation; autoregressive modeling methods; noncontiguous measurements; power spectral density; segmented time signals; stationary time series; Density measurement; Electric variables measurement; Entropy; Power engineering and energy; Power engineering computing; Power measurement; Random processes; Signal processing; Signal sampling; Time measurement;
fLanguage
English
Publisher
ieee
Conference_Titel
System Theory, 1990., Twenty-Second Southeastern Symposium on
Conference_Location
Cookeville, TN
ISSN
0094-2898
Print_ISBN
0-8186-2038-2
Type
conf
DOI
10.1109/SSST.1990.138204
Filename
138204
Link To Document