Estimation of randomly sampled sinusoids in additive noise

A solution to the problem of estimating the frequencies, amplitudes and phases of the underlying sinusoidal components is offered for signals consisting of real sinusoids in additive white noise. In order to obtain the data required for frequency estimation, uniform sample points from the randomly spaced samples are first interpolated with the aid of the singular value decomposition. One can then estimate the line spectrum of the underlying sinusoidal signal using principal component, autoregressive modeling and determine the amplitudes and phases through linear least squares. This method is shown by simulation to compare favorably to modern frequency estimators and the Cramer-Rao lower bound