Asymptotic statistics for a generalized frequency-wavenumber estimator

The estimation of the frequency-wavenumber spectrum form the output of a sensor array placed in a homogeneous random field is considered. A generalized estimator which includes the Bartlett and minimum-variance estimators is used. Joint asymptotic normality of this frequency-wavenumber estimator is...

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Bibliographic Details
Published in:IEEE transactions on acoustics, speech, and signal processing speech, and signal processing, 1990-05, Vol.38 (5), p.804-813
Main Authors: Gingras, D.F., Hobbs, S.L.
Format: Article
Language:English
Subjects:
Acoustic sensors
Applied sciences
Covariance matrix
Delay estimation
Detection, estimation, filtering, equalization, prediction
Exact sciences and technology
Frequency estimation
Information, signal and communications theory
Maximum likelihood estimation
Oceans
Sensor arrays
Signal and communications theory
Signal, noise
Sonar
Statistical analysis
Statistics
Telecommunications and information theory
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Summary:The estimation of the frequency-wavenumber spectrum form the output of a sensor array placed in a homogeneous random field is considered. A generalized estimator which includes the Bartlett and minimum-variance estimators is used. Joint asymptotic normality of this frequency-wavenumber estimator is established; a precise asymptotic expression for the covariance matrix of the limiting distribution is obtained. It is shown that the generalized estimator converges in the mean-square sense. Confidence intervals based on the asymptotic normality results are presented for two array configuration examples.< >
ISSN:0096-3518
DOI:10.1109/29.56026