Title :
Spectral Analysis of Non-Uniformly Sampled Data: A New Approach Versus the Periodogram
Author :
He, Hao ; Li, Jian ; Stoica, Petre
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL
Abstract :
We begin by revisiting the plain least-squares periodogram (LSP) for real-valued data. Then we introduce a new method for spectral analysis of non-uniformly sampled data by "iteratively weighting LSP", and we name the new method real-valued iterative adaptive approach (RIAA). LSP and RIAA are most suitable for data sequences with discrete spectra. For such type of data, we present a procedure to obtain a parametric spectral estimate, from the LSP or RIAA non-parametric estimate, by means of the Bayesian information criterion (BIC). We also discuss a possible strategy for designing the sampling pattern of future measurements. Several numerical examples are provided to illustrate the performance of our proposed approaches.
Keywords :
Bayes methods; data analysis; iterative methods; least squares approximations; nonparametric statistics; spectral analysis; Bayesian information criterion; data sequences; discrete spectra; least-squares periodogram; nonparametric estimation; nonuniformly sampled data; parametric spectral estimate; real-valued data; real-valued iterative adaptive approach; sampling pattern; spectral analysis; Biomedical measurements; Data engineering; Design methodology; Extraterrestrial measurements; Helium; Information technology; Iterative methods; Sampling methods; Spectral analysis; Testing; BIC; Spectral analysis; non-uniformly sampled data; the iterative adaptive approach; the least-squares method; the periodogram;
Conference_Titel :
Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, 2009. DSP/SPE 2009. IEEE 13th
Conference_Location :
Marco Island, FL
Print_ISBN :
978-1-4244-3677-4
Electronic_ISBN :
978-1-4244-3677-4
DOI :
10.1109/DSP.2009.4785952
