Power spectrum estimation from values of noisy autocorrelations

L. Rebollo Neira; A.G. Constantinides

doi:10.1016/0165-1684(96)00025-4

Power spectrum estimation from values of noisy autocorrelations

L. Rebollo Neira, A.G. Constantinides

Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

The problem of estimating the power spectrum from noisy autocorrelation values is considered in this paper, and it is proposed that in order to reduce errors, oversampling of the available time domain data should be employed. The oversampling problem is discussed from the frame theory point of view, and it is shown that the frame reconstruction represents an improvement upon the standard correlogram, windowing, and autoregressive modelling approaches.

Original language	English
Pages (from-to)	223-231
Number of pages	9
Journal	Signal processing
Volume	50
Issue number	3
DOIs	https://doi.org/10.1016/0165-1684(96)00025-4
Publication status	Published - May 1996

Bibliographical note

Keywords

power spectrum
noisy autocorrelation values
errors
oversampling problem
frame theory
standard correlogram
windowing
autoregressive modelling approaches

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10.1016/0165-1684(96)00025-4

Cite this

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title = "Power spectrum estimation from values of noisy autocorrelations",

abstract = "The problem of estimating the power spectrum from noisy autocorrelation values is considered in this paper, and it is proposed that in order to reduce errors, oversampling of the available time domain data should be employed. The oversampling problem is discussed from the frame theory point of view, and it is shown that the frame reconstruction represents an improvement upon the standard correlogram, windowing, and autoregressive modelling approaches.",

keywords = "power spectrum, noisy autocorrelation values, errors, oversampling problem, frame theory, standard correlogram, windowing, autoregressive modelling approaches",

author = "{Rebollo Neira}, L. and A.G. Constantinides",

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AB - The problem of estimating the power spectrum from noisy autocorrelation values is considered in this paper, and it is proposed that in order to reduce errors, oversampling of the available time domain data should be employed. The oversampling problem is discussed from the frame theory point of view, and it is shown that the frame reconstruction represents an improvement upon the standard correlogram, windowing, and autoregressive modelling approaches.

KW - power spectrum

KW - noisy autocorrelation values

KW - errors

KW - oversampling problem

KW - frame theory

KW - standard correlogram

KW - windowing

KW - autoregressive modelling approaches

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JO - Signal processing

JF - Signal processing

IS - 3

ER -

Power spectrum estimation from values of noisy autocorrelations

Abstract

Bibliographical note

Keywords

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