Abstract
The S-transform (ST) is a method of time-frequency analysis of time series. We develop here the Multi-taper S-transform (MTST) for spectral estimation of multi-variate stationary processes through replacing the scalable Gaussian window of the ST with scalable, adjustable orthogonal time-frequency Hermite functions. The MTST is shown to reduce bias and variance of power spectral density (PSD) estimates and coherence over the entire frequency domain and compares favourably with the estimates obtained with the Welch and Multi-taper methods. The MTST method has been successfully applied to data from two anemometers in Hong Kong, during Typhoon Mangkhut, for the estimation of PSD and coherence.
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