Wavelet estimation of cyclospectra

In this paper, we propose wavelet-thresholding estimators for spectrum analysis of a zero-mean cyclostationary signal. In the case of Gaussian regression, it is known that wavelet estimators outperform traditional linear methods if the regularity of the function to be estimated varies substantially over its domain of definition. The goal of this paper is to extend these wavelet methods to the estimation of cyclospectra. In this context, we show both theoretically and through a simulation example that wavelet-thresholding estimators lead to improved performance compared to kernel methods.