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A Nonuniform sampled data, , least-squares method, iterative adaptive approach,, periodogram,

The spectral analysis of non uniform sampled data sequences using Fourier Periodogram method is the classical approach. In view of data fitting and computational standpoints why the Least squares periodogram(LSP) method is preferable than the “classical” Fourier periodogram and as well as to the frequently-used form of LSP due to Lomb and Scargle is explained. Then a new method of spectral analysis of nonuniform data sequences can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. It is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the iterative adaptive approach (IAA).LSP and IAA are nonparametric methods that can be used for the spectral analysis of general data sequences with both continuous and discrete spectra. However, they are most suitable for data sequences with discrete spectra (i.e., sinusoidal data), which is the case we emphasize in this paper. Of the existing methods for nonuniform sinusoidal data, Welch, MUSIC and ESPRIT methods appear to be the closest in spirit to the IAA proposed here. Indeed, all these methods make use of the estimated covariance matrix that is computed in the first iteration of IAA from LSP. MUSIC and ESPRIT, on the other hand, are parametric methods that require a guess of the number of sinusoidal components present in the data, otherwise they cannot be used; furthermore.