Wavelet-based methods for clutter removal from radar wind profiler data

Abstract

The common way to process radar wind profiler (RWP) data by moments estimation of the Fourier power spectrum fails in presence of transient intermittent clutter contributions. Wavelets are especially suitable for detecting and removing transient components because of their high localization in time and frequency domain. We give an overview on the wavelet filtering of contaminated discrete RWP signals and introduce a new technique involving the wavelet packet decomposition and a splitting in progressive and regressive signal components. This technique has been successfully tested on severely real-data sets where classical wavelet routines fail.