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Estimation of Spectral Moments for Weather Echoes

Dušan S. Zrnić

Open publisher page 244 citations

Abstract

Estimators of echo signal power, mean Doppler velocity, and spectrum width are investigated. Maximum likelihood (ML) solutions can improve the estimate variances by an order of magnitude over those by the autocovariance or the Fourier method. However, the required computations are excessive for routine implementations on weather radars. Spectrum power estimation together with two conventional methods of mean Doppler and width estimation are reviewed. Several results previously not available are presented.

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What this paper is about

Estimators of echo signal power, mean Doppler velocity, and spectrum width are investigated. Maximum likelihood (ML) solutions can improve the estimate variances by an order of magnitude over those by the autocovariance or the Fourier method. However, the required computations are excessive for routine implementations on weather radars. Spectrum power estimation together with two conventional methods of mean Doppler and width estimation are reviewed. Several results previously not available are presented.

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OpenAlex reports 244 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Estimators of echo signal power, mean Doppler velocity, and spectrum width are investigated. Maximum likelihood (ML) solutions can improve the estimate variances by an order of magnitude over those by the autocovariance or the Fourier method. However, the required computations are excessive for routine implementations on weather radars. Spectrum power estimation together with two conventional methods of mean Doppler and width estimation are reviewed. Several results previously not available are presented.

Key concepts: Autocovariance, Estimator, Spectral density estimation, Spectral density, Doppler effect, Maximum likelihood, Computation, Mathematics

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