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Computationally efficient suboptimum ARMA spectral estimation

M. Kaveh, S.P. Bruzzone

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Abstract

There is a great deal of interest in data-adaptive spectral estimation. The general appeal of these methods stems from the fact that their resolution is not determined by a spectral window bandwidth (which depends on the data length) as in the traditional Blackman and Tukey estimators. The method most commonly used, is the autoregressive (AR) or the maximum entropy spectral estimator.

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What this paper is about

There is a great deal of interest in data-adaptive spectral estimation. The general appeal of these methods stems from the fact that their resolution is not determined by a spectral window bandwidth (which depends on the data length) as in the traditional Blackman and Tukey estimators. The method most commonly used, is the autoregressive (AR) or the maximum entropy spectral estimator.

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Available abstract

There is a great deal of interest in data-adaptive spectral estimation. The general appeal of these methods stems from the fact that their resolution is not determined by a spectral window bandwidth (which depends on the data length) as in the traditional Blackman and Tukey estimators. The method most commonly used, is the autoregressive (AR) or the maximum entropy spectral estimator.

Key concepts: Maximum entropy spectral estimation, Spectral density estimation, Estimator, Autoregressive model, Spectral analysis, Computer science, Algorithm, Principle of maximum entropy

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