The Fast Fourier Transform for Experimentalists Part III: Classical Spectral Analysis

Authors:
Bert Rust;Denis Donnelly
Affiliations:
Siena College;US National Institute for Standards and Technology
Venue:
Computing in Science and Engineering
Year:
2005

Citing 3
Cited 3

The Fast Fourier Transform for Experimentalists, Part I: Concepts

Computing in Science and Engineering
The Fast Fourier Transform for Experimentalists, Part II: Convolutions

Computing in Science and Engineering
Extrapolation, Interpolation, and Smoothing of Stationary Time Series

Extrapolation, Interpolation, and Smoothing of Stationary Time Series

The Fast Fourier Transform for Experimentalists, Part IV: Autoregressive Spectral Analysis

Computing in Science and Engineering
The Fast Fourier Transform for Experimentalists, Part V: Filters

Computing in Science and Engineering
The Fast Fourier Transform for Experimentalists, Part VI: Chirp of a Bat

Computing in Science and Engineering

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Abstract

In Part I, we introduced the idea of windowing a time series before estimating its frequency spectrum. In addition to some fundamental elements, we examined zero padding, aliasing, the relationship to a Fourier series, and windowing. One disadvantage of windowing is that it alters or restricts the data, which, of course, has consequences for the spectral estimate. In this installment, we continue our discussion from that first installment with a more general approach to computing spectrum estimates via the FFT.