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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