Power Spectrum Estimation
Power spectrum reveals the existence, or the absence, of repetitive patterns and correlation structures in a signal process. These structural patterns are important in a wide range of applications such as data forecasting, signal coding, signal detection, radar, pattern recognition, and decision making systems. The most common method of spectral estimation is based on the fast Fourier transform (FFT). For many applications, FFT-based methods produce sufficiently good results. However, more advanced methods of spectral estimation can offer better frequency resolution, and less variance. This chapter begins with an introduction to the Fourier transform and the basic principles of spectral estimation. The classical methods for power spectrum estimation are based on periodograms. Various methods of averaging periodograms, and their effects on the variance of spectral estimates, are considered. We then study the maximum entropy, and the model based spectral estimation methods. We also consider several high-resolution spectral estimation methods, based on eigen analysis, for estimation of sinusoids observed in additive white noise.
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- Blackman R. B., Tukey J. W. (1958), The Measurement of Power Spectra from the Point of View of Communication Engineering, Dover Publications, New York.Google Scholar
- Burg J. P. (1975), Maximum Entropy Spectral Analysis, PhD Thesis, Department of Geophysics, Stanford University, California.Google Scholar
- Childers D. G., Editor (1978), Modern Spectrum Analysis, IEEE Press New York.Google Scholar
- Cadzow J. A. (1979), ARMA Spectral Estimation: An Efficient Closed-form Procedure, Proc. RADC Spectrum estimation Workshop, Pages 81–97.Google Scholar
- Kay S. M., Marple S. L., (1981) Spectrum Analysis: A Modern Perspective Proceedings of the IEEE, Vol. 69, Pages 1380–1419.Google Scholar
- Lacoss R. T. (1971), Data Adaptive Spectral Analysis Methods, Geophysics, Vol. 36, Pages 661–675.Google Scholar
- Marple S. L. (1987) Digital Spectral Analysis with Applications. Prentice Hall-Englewood Cliffs, N.J.Google Scholar
- Roy R. H. (1987), ESPRIT-Estimation of Signal Parameters via Rotational Invariance Techniques. PhD Thesis, Stanford University, California.Google Scholar
- Schmidt R. O. (1981), A signal Subspace Approach to Multiple Emitter Location and Spectral Estimation, PhD Thesis, Stanford University, California.Google Scholar
- Van Den Bos A. (1971) “Alternative Interpretation of Maximum Entropy Spectral Analysis”, IEEE Trans. Infor. Tech., Vol. IT-17, Pages 92–93.Google Scholar