Title :
Spectral Analysis of Nonuniformly Sampled Data: A New Approach Versus the Periodogram
Author :
Stoica, Petre ; Li, Jian ; He, Hao
Author_Institution :
Dept. of Inf. Technol., Uppsala Univ., Uppsala
fDate :
3/1/2009 12:00:00 AM
Abstract :
We begin by revisiting the periodogram to explain why arguably the plain least-squares periodogram (LSP) is preferable to the ldquoclassicalrdquo Fourier periodogram, from a data-fitting viewpoint, as well as to the frequently-used form of LSP due to Lomb and Scargle, from a computational standpoint. Then we go on to introduce a new enhanced method for spectral analysis of nonuniformly sampled data sequences. The new method can be interpreted as an iteratively weighted LSP that makes use of a data-dependent weighting matrix built from the most recent spectral estimate. Because this method is derived for the case of real-valued data (which is typically more complicated to deal with in spectral analysis than the complex-valued data case), it is iterative and it makes use of an adaptive (i.e., data-dependent) weighting, we refer to it as the real-valued iterative adaptive approach (RIAA). LSP and RIAA 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.
Keywords :
Fourier transforms; data handling; iterative methods; least squares approximations; spectral analysis; Fourier periodogram; data sequences; data-dependent weighting matrix; data-fitting viewpoint; iteratively weighted LSP; least-squares periodogram; nonuniformly sampled data; real-valued iterative adaptive approach; spectral analysis; BIC; iterative adaptive approach; least-squares method; nonuniformly sampled data; periodogram; spectral analysis;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2008.2008973