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
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;
Conference_Titel :
System Theory, 1990., Twenty-Second Southeastern Symposium on
Conference_Location :
Cookeville, TN
Print_ISBN :
0-8186-2038-2
DOI :
10.1109/SSST.1990.138204
