Approximate ML Estimation of the Period and Spectral Content of Multiharmonic Signals Without User Interaction

The goal of this paper is to construct an approximate maximum-likelihood estimator to accurately estimate the period and spectral contents of a noisy periodic signal that has many frequency components. This is accomplished without user interaction. The signal data record has a total number of periods that is not necessarily an integer but is greater than four. Furthermore, the number of samples per period may not necessarily be an integer number. It is shown that the accuracy of the estimated results is superior to estimates that are devoid of variance weighting, such as those engendered by the least squares estimator.