| |
| |
|
|
|
|
| A New Enhanced Method of Non Parametric power spectrum Estimation.
|
|
Full
text: |
 PDF(483.6KB) |
|
|
Source |
Signal Processing: An International Journal (SPIJ) |
|
Table of Contents |
|
|
Download
Complete Issue PDF(1.86MB) |
|
Volume: 4 Issue: 1 |
| |
Pages: 1-67 |
|
Publication
Date: March 2010 |
|
ISSN
(Online): 1985-2339 |
|
|
|
|
|
Pages |
38 - 53 |
|
Author(s) |
|
|
|
Published
Date |
08-04-2010 |
|
Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
|
ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
| |
|
| |
KEYWORDS: A Nonuniform sampled data, , least-squares method, iterative adaptive approach,, periodogram, |
|
|
| |
|
|
| This Manuscript is indexed in the following databases/websites:- |
|
| 1. Search-Docs |
| 2. Docstoc |
| 3. Directory of Open Access Journals (DOAJ) |
| 4. PDFCAST |
| 5. Scribd |
| 6. Google Scholar |
| 7. Bielefeld Academic Search Engine (BASE) |
| 8. refSeek |
| 9. Academic Index |
| 10. iSEEK |
| 11. Socol@r |
| |
|
| |
|
|
| 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. |
| |
|
| |
|
| |
| 1 |
Stoica,P and R.L. Moses, Introduction to Spectral Analysis, Prentice-Hall, 1997, pp. 24-26.
|
|
|
| 2 |
Welch, P.D, "The Use of Fast Fourier Transform for the Estimation of Power Spectra: A Method Based on Time Averaging Over Short, Modified Periodogram," IEEE Trans. Audio electro acoustics, Vol. AU-15 (June 1967), pp. 70-73.
|
|
|
| 3 |
Oppenheim, A.V., and R.W. Schafer, Discrete-Time Signal Processing, Prentice-Hall, 1989, pp. 730- 742.
|
|
|
| 4 |
B. Priestley, Spectral Analysis and Time Series, Volume 1: Univariate Series, New York: Academic, 1981.
|
|
|
| 5 |
P. Stoica and R. L. Moses, Spectral Analysis of Signals Upper Saddle River, NJ: Prentice-Hall, 2005.
|
|
|
| 6 |
F.J.M.Barning,“The numerical analysis of the light-curve of12 lacerate,” Bull.Astronomy. Inst Netherlands, vol. 17, no. 1, pp. 22–28, Aug. 1963.
|
|
|
| 7 |
P. Vanicek, “Approximate spectral analysis by least-squares fit,” Astro- phys. Space Sci., vol. 4, no. 4, pp. 387–391, Aug. 1969.
|
|
|
| 8 |
P. Vanicek, “Further development and properties of the spectral anal- ysis by least- squares,” Astrophys. Space Sci.vol. 12, no. 1, pp. 10–33, Jul. 1971.
|
|
|
| 9 |
N. R. Lomb, “Least-squares frequency analysis of unequally spaced data,” Astrophysics . Space Sci., vol. 39, no. 2, pp. 447–462, Feb. 1976.
|
|
|
| 10 |
S. Ferraz-Mello, “Estimation of periods from unequally spaced obser- vations,” Astronom. J., vol.86, no.4, pp. 619–624, Apr. 1981.
|
|
|
| 11 |
J. D. Scargle, “Studies in astronomical time series analysis. II—Statis- tical aspects of spectral analysis of unevenly spaced data,” Astrophys. J., vol. 263, pp 835–853, Dec. 1982.
|
|
|
| 12 |
W. H. Press and G. B. Rybicki, “Fast algorithm for spectral analysis of unevenly sampled data,” Astrophys. J., vol. 338, pp. 277–280, Mar.1989.
|
|
|
| 13 |
J. A. Fessler and B. P. Sutton, “Nonuniform fast Fourier transforms using min-max interpolation,” IEEE Trans. Signal Process., vol. 51, no. 2, pp. 560–574, Feb. 2003.
|
|
|
| 14 |
N. Nguyen and Q. Liu, “The regular Fourier matrices and nonuniform fast Fourier transforms,” SIAM J. Sci. Comput., vol. 21, no. 1,pp. 283–293, 2000.
|
|
|
| 15 |
L. Eyer and P. Bartholdi, “Variable stars: Which Nyquist frequency?,”Astron. Astrophys. Supp Series, vol. 135, pp. 1–3, Feb. 1999.
|
|
|
| 16 |
F. A. M. Frescura, C. A. Engelbrecht, and B. S. Frank, “Significance tests for periodogram peaks,” NASA Astrophysics Data System, Jun. 2007
|
|
|
| 17 |
P. Reegen, “SigSpec—I. Frequency- and phase resolved significance in Fourier space,” Astron Astrophys., vol. 467, pp. 1353–1371, Mar.2007.
|
|
|
| 18 |
J. Li and P. Stoica, “An adaptive filtering approach to spectral estimation and SAR imaging,”Trans., vol. 44, no. 6,pp. 1469–1484, Jun. 1996.
|
|
|
| 19 |
T. Yardibi, M. Xue, J. Li, P. Stoica, and A. B.Baggeroer, “Iterative adaptive approach for sparse signal representation with sensing applications,” IEEE Trans. Aerosp. Electron . Syst., 2007.
|
|
|
| 20 |
P. Stoica and Y. Selen, “Model-order selection: A review of information criterion rules,” IEEE Signal Process. Mag., vol. 21, no. 4, pp.36–47, Jul. 2004.
|
|
|
| 21 |
E. S. Saunders, T. Naylor, and A. Allan, “Optimal placement of a limited number of observations for period searches,” Astron. Astrophys.,vol. 455, pp. 757–763, May 2006.
|
|
|
| |
|
| |
|
| |
| |
|
| |
|
| |
| |
|
| |
|
| |
|
| K.Suresh Reddy : Colleagues
|
|
| S.Venkata Chalam : Colleagues
|
|
| B.C.Jinaga : Colleagues
|
|
|
|
|
|
|
|
|
|
|
