Spectral Estimation from Irregularly Sampled Data for Frequencies Far Above the Mean Data Rate

Slotted resampling transforms an irregularly sampled process into an equidistant missing-data problem. Equidistant resampling inevitably causes bias, due to the shift of the observation times. Using a slot width smaller than the resampling time can diminish that bias for the same frequency range. A dedicated estimator for time series models of multiple slotted data sets with missing observations has been developed for the estimation of the power spectral density and of the autocorrelation function. The algorithm estimates time series models and selects the order and type from a number of candidates. It is tested with benchmark data. Without any problems spectra can be estimated up to frequencies higher than 100 times the mean data rate.