DocumentCode :
1433508
Title :
A statistical resolution theory of the AR method of spectral analysis
Author :
Zhang, Q.T.
Author_Institution :
Dept. of Electr. Eng., Toronto Univ., Ont., Canada
Volume :
46
Issue :
10
fYear :
1998
fDate :
10/1/1998 12:00:00 AM
Firstpage :
2757
Lastpage :
2766
Abstract :
The autoregressive (AR) method of spectral analysis is widely used in diverse areas for its solid theoretical foundation, interesting physical interpretation, computational efficiency, and, more importantly, high resolution capability. Various aspects of its statistical performance have been investigated. However, the resolution probability that provides the most rigorous description of the spectrum resolution capability is still not available in the literature. In this paper, by formulating the resolution event in the framework of statistical decision theory and directly determining its probability from its characteristic function, we obtain an exact asymptotic formula for the probability of resolution. On this basis, we determine the limiting resolving behavior of the sample AR spectrum and develop the corresponding geometrical insight in the parametric space. Simulation and numerical results are also presented to confirm and illustrate the theory
Keywords :
autoregressive processes; decision theory; parameter estimation; probability; signal resolution; signal sampling; spectral analysis; statistical analysis; AR method; autoregressive method; characteristic function; computational efficiency; exact asymptotic formula; high resolution; limiting resolving behavior; narrowband signals; parametric space; resolution probability; sample AR spectrum; signal processing; simulation results; spectral analysis; spectrum resolution; statistical decision theory; statistical performance; statistical resolution theory; Computational efficiency; Decision theory; Entropy; Filters; Frequency; Limiting; Probability; Solids; Spectral analysis; White noise;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/78.720377
Filename :
720377
Link To Document :
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