DocumentCode :
1099476
Title :
L1-Norm Convergence Properties of Correlogram Spectral Estimates
Author :
Casinovi, Giorgio
Author_Institution :
Georgia Inst. of Technol., Georgia
Volume :
55
Issue :
9
fYear :
2007
Firstpage :
4354
Lastpage :
4365
Abstract :
This paper establishes the following results concerning the estimation of the power spectrum of a single, deterministic, infinitely long signal. a) If S x is the signal´s power spectral density, correlogram spectral estimates obtained from increasingly longer signal segments tend to S x * ? x/2p in the L 1-norm, where ? is the Fourier transform of the window used to generate the estimates. b) The L 1-norm of S x - S x * ? x/2p can be made arbitrarily small by an appropriate choice of window. It is further shown that the accuracy of the spectral estimates generated by a given window is related to a newly introduced function, termed the windowing error kernel and that this function yields bounds on the asymptotic error of the estimates. As an example, correlogram spectral estimates are used to analyze spectral regrowth in an amplifier.
Keywords :
Fourier transforms; amplifiers; correlation methods; error analysis; estimation theory; nonlinear distortion; spectral analysis; L 1 -norm convergence property; Fourier transform; amplifier spectral regrowth analysis; asymptotic error; correlogram spectral estimate; nonlinear distortion; numerical analysis; power spectrum estimation; signal power spectral density; windowing error kernel; Convergence; Fourier transforms; Power generation; Power system modeling; Signal analysis; Signal generators; Signal processing; Spectral analysis; Stochastic processes; Yield estimation; Estimation; Fourier transforms; nonlinear distortion; numerical analysis; spectral analysis; windowing;
fLanguage :
English
Journal_Title :
Signal Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1053-587X
Type :
jour
DOI :
10.1109/TSP.2007.896257
Filename :
4291844
Link To Document :
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