The preceding analysis can be extended to the case of *multiple*
sinusoids in white noise [120]. When the sinusoids are
*well resolved*, *i.e.*, when window-transform side lobes are
negligible at the spacings present in the signal, the optimal
estimator reduces to finding multiple interpolated peaks in the
spectrum.

One exact special case is when the sinusoid frequencies coincide with the ``DFT frequencies'' , for . In this special case, each sinusoidal peak sits atop a zero crossing in the window transform associated with every other peak.

To enhance the ``isolation'' among multiple sinusoidal peaks, it is natural to use a window function which minimizes side lobes. However, this is not optimal for short data records since valuable data are ``down-weighted'' in the analysis. Fundamentally, there is a trade-off between peak estimation error due to overlapping side lobes and that due to widening the main lobe. In a practical sinusoidal modeling system, not all sinusoidal peaks are recovered from the data--only the ``loudest'' peaks are measured. Therefore, in such systems, it is reasonable to assure (by choice of window) that the side-lobe level is well below the ``cut-off level'' in dB for the sinusoidal peaks. This prevents side lobes from being comparable in magnitude to sinusoidal peaks, while keeping the main lobes narrow as possible.

When multiple sinusoids are close together such that the associated main lobes overlap, the maximum likelihood estimator calls for a nonlinear optimization. Conceptually, one must search over the possible superpositions of the window transform at various relative amplitudes, phases, and spacings, in order to best ``explain'' the observed data.

Since the number of sinusoids present is usually not known, the number
can be estimated by means of *hypothesis testing* in a Bayesian
framework [21]. The ``null hypothesis'' can be ``no
sinusoids,'' meaning ``just white noise.''

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