Doppler spectrum width relates to wind shear and turbulence and thus is an important parameter which characterizes severe weather phenomena. The need to scan large volumes of space quickly and to obtain real-time estimates introduces stringent requirements on the signal processing. The Fourier transform and the autocovariance methods are candidate techniques. In particular, the autocovariance estimator is attractive due to low storgage and ease of computation. Statistics of an improved and asymptotically ubiased (for Gaussian spectra) autocovariance estimator of width are presented. Spurious effects on the complex video signal such as dc offsets and imbalances are assessed. Performance of the improved estimator on weather data is shown.
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