Please use this identifier to cite or link to this item: http://kb.psu.ac.th/psukb/handle/2010/7191
Title: Spectrum analysis by autoregressive methods: Performance on application to stationary signals
Authors: Kamata, Minoru
Ngamsritragul, Panyarak
Mechanical Engineering
Keywords: Signal Analysis;Spectrum Analysis;Time Series Analysis;Signal Processing;AR Method
Issue Date: 1996
Publisher: The Japan Society of Mechanical Engineers
Citation: JSME international journal. Ser. C, Dynamics, control, robotics, design and manufacturing 39-C(2), 179-187, 1996-06-15
Series/Report no.: JSME international journal. Ser. C, Dynamics, control, robotics, design and manufacturing;
Abstract: In order to develop a method capable of determining the time variant spectrum of time series, various existing approaches have been investigated. Although the Fourier-based methods are superior in their computational efficiency, their inherent characteristics may sometimes limit applications. The AR method gives the best results even for small data sets. However, insufficient information is available for determining its applicability. In this report, a brief review, as well as the performance, of various AR methods applied to a certain class of stationary time series is systematically documented. The covariance method is found to be the best solution for the determination of AR coefficients, and many trials using sinusoidal data sets indicate the usefullness and applicability of AR-based spectrum analysis.
URI: http://kb.psu.ac.th/psukb/handle/2010/7191
ISSN: 1340-8062
Appears in Collections:215 Articles

Files in This Item:
File Description SizeFormat 
110004089522.pdf817.38 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons