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Differentiating noisy radiocommunications signals: wavelet estimation of a derivative in the presence of heteroscedastic noise

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Radio scientists require estimates of the rate of change in rain-induced signals. Unfortunately, these signals are observed in the presence of atmospheric noise, which has a variance that is dependent on temperature, pressure and other climatic variables. We develop a systematic approach to the problem, using wavelet differentiation combined with coefficient-dependent thresholding, and illustrate the considerable benefits that this provides over more conventional techniques.
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Keywords: Heteroscedastic noise; Linear inverse problems; Radiocommunications signals; Wavelet differentiation; Wavelet thresholding

Document Type: Research Article

Affiliations: 1: University of Leeds, UK 2: University of Essex, Colchester, UK

Publication date: 2005-08-01

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