Fourier transform

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The Fourier transform, named after Joseph Fourier, is an integral transform that re-expresses a function in terms of sinusoidal basis functions, i.e. as a sum or integral of sinusoidal functions multiplied by some coefficients ("amplitudes"). There are many closely-related variations of this transform, summarized below, depending upon the type of function being transformed. See also: List of Fourier-related transforms.

Applications

Fourier transforms have many scientific applications — in physics, number theory, combinatorics, signal processing, probability theory, statistics, cryptography, acoustics, oceanography, optics, geometry, and other areas. (In signal processing and related fields, the Fourier transform is typically thought of as decomposing a signal into its component frequencies and their amplitudes.) This wide applicability stems from several useful properties of the transforms:

  • The transforms are invertible, and in fact the inverse transform has almost the same form as the forward transform.
  • The sinusoidal basis functions are eigenfunctions of differentiation, which means that this representation transforms linear differential equations with constant coefficients into ordinary algebraic ones. (For example, in a linear time-invariant physical system, frequency is a conserved quantity, so the behavior at each frequency can be solved independently.)
  • The discrete version of the Fourier transform (see below) can be evaluated quickly on computers using fast Fourier transform (FFT) algorithms.

Variants of the Fourier transform

Continuous Fourier transform

Most often, the unqualified term "Fourier transform" refers to the continuous Fourier transform, representing any square-integrable function f(t) as a sum of complex exponentials with angular frequencies ω and complex amplitudes F(ω):

f(t)={\mathcal  {F}}^{{-1}}(F)(t)={\frac  {1}{{\sqrt  {2\pi }}}}\int \limits _{{-\infty }}^{\infty }F(\omega )e^{{i\omega t}}\,d\omega .

This is actually the inverse continuous Fourier transform, whereas the Fourier transform expresses F(ω) in terms of f(t); the original function and its transform are sometimes called a transform pair. See continuous Fourier transform for more information, including a table of transforms, discussion of the transform properties, and the various conventions. A generalization of this transform is the fractional Fourier transform, by which the transform can be raised to any real "power".

When f(t) is an even or odd function, the sine or cosine terms disappear and one is left with the cosine transform or sine transform, respectively. Another important case is where f(t) is purely real, where it follows that F(−ω) = F(ω)*. (Similar special cases appear for all other variants of the Fourier transform as well.)

Fourier series

The continuous transform is itself actually a generalization of an earlier concept, a Fourier series, which was specific to periodic (or finite-domain) functions f(x) (with period 2π), and represents these functions as a series of sinusoids:

f(x)=\sum _{{n=-\infty }}^{{\infty }}F_{n}\,e^{{inx}},

where F_{n} is the (complex) amplitude. Or, for real-valued functions, the Fourier series is often written:

f(x)={\frac  {1}{2}}a_{0}+\sum _{{n=1}}^{\infty }\left[a_{n}\cos(nx)+b_{n}\sin(nx)\right],

where an and bn are the (real) Fourier series amplitudes.

Discrete Fourier transform

For use on computers, both for scientific computation and digital signal processing, one must have functions xk that are defined over discrete instead of continuous domains, again finite or periodic. In this case, one uses the discrete Fourier transform (DFT), which represents xk as the sum of sinusoids:

x_{k}={\frac  {1}{n}}\sum _{{j=0}}^{{n-1}}f_{j}e^{{2\pi ijk/n}}\quad \quad k=0,\dots ,n-1

where fj are the Fourier amplitudes. Although applying this formula directly would require O(n2) operations (see Big O notation), it can be computed in only O(n log n) operations using a fast Fourier transform (FFT) algorithm, which makes FFT a practical and important operation on computers.

Other variants

The DFT is a special case of (and is sometimes used as an approximation for) the discrete-time Fourier transform (DTFT), in which the xk are defined over discrete but infinite domains, and thus the spectrum is continuous and periodic. The DTFT is essentially the inverse of the Fourier series.

These Fourier variants can also be generalized to Fourier transforms on arbitrary locally compact abelian topological groups, which are studied in harmonic analysis; there, one transforms from a group to its dual group. This treatment also allows a general formulation of the convolution theorem, which relates Fourier transforms and convolutions. See also the Pontryagin duality for the generalized underpinnings of the Fourier transform.

Time-frequency transforms such as the short-time Fourier transform, wavelet transforms, chirplet transforms, and the fractional Fourier transform try to obtain frequency information from a signal as a function of time (or whatever the independent variable is), although the ability to simultaneously resolve frequency and time is limited by a (mathematical) uncertainty principle.

Family of Fourier transforms

The following table summarizes the family of Fourier transforms. We see that discreteness in one domain implies periodicity in the conjugate domain and that continuity in one domain implies aperiodicity in the conjugate domain. Moreover: reality in one domain implies symmetry in the conjugate domain.

Transform Time Frequency
Continuous Fourier transform Continuous, Aperiodic Continuous, Aperiodic
Fourier series Continuous, Periodic Discrete, Aperiodic
Discrete-time Fourier transform Discrete, Aperiodic Continuous, Periodic
Discrete Fourier transform Discrete, Periodic Discrete, Periodic

Interpretation in terms of time and frequency

In terms of signal processing, the transform takes a time series representation of a signal function and maps it into a frequency spectrum, where ω is angular frequency. That is, it takes a function in the time domain into the frequency domain; it is a decomposition of a function into harmonics of different frequencies.

When the function f is a function of time and represents a physical signal, the transform has a standard interpretation as the frequency spectrum of the signal. The magnitude of the resulting complex-valued function F represents the amplitudes of the respective frequencies (ω), while the phase shifts are given by arctan(imaginary parts/real parts).

However, it is important to realize that Fourier transforms are not limited to functions of time, and temporal frequencies. They can equally be applied to analyze spatial frequencies, and indeed for nearly any function domain.

References

  • Smith, Steven W. The Scientist and Engineer's Guide to Digital Signal Processing, 2nd edition. San Diego: California Technical Publishing, 1999. ISBN 0-9660176-3-3. (also available online: [1])
  • A. D. Polyanin and A. V. Manzhirov, Handbook of Integral Equations, CRC Press, Boca Raton, 1998. ISBN 0-8493-2876-4

See also

External links

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