Fractional Fourier transform explained

In mathematics, in the area of harmonic analysis, the fractional Fourier transform (FRFT) is a family of linear transformations generalizing the Fourier transform. It can be thought of as the Fourier transform to the n-th power, where n need not be an integer - thus, it can transform a function to any intermediate domain between time and frequency. Its applications range from filter design and signal analysis to phase retrieval and pattern recognition.

The FRFT can be used to define fractional convolution, correlation, and other operations, and can also be further generalized into the linear canonical transformation (LCT). An early definition of the FRFT was introduced by Condon,^[1] by solving for the Green's function for phase-space rotations, and also by Namias,^[2] generalizing work of Wiener^[3] on Hermite polynomials.

However, it was not widely recognized in signal processing until it was independently reintroduced around 1993 by several groups.^[4] Since then, there has been a surge of interest in extending Shannon's sampling theorem^{[5] [6]} for signals which are band-limited in the Fractional Fourier domain.

A completely different meaning for "fractional Fourier transform" was introduced by Bailey and Swartztrauber^[7] as essentially another name for a z-transform, and in particular for the case that corresponds to a discrete Fourier transform shifted by a fractional amount in frequency space (multiplying the input by a linear chirp) and evaluating at a fractional set of frequency points (e.g. considering only a small portion of the spectrum). (Such transforms can be evaluated efficiently by Bluestein's FFT algorithm.) This terminology has fallen out of use in most of the technical literature, however, in preference to the FRFT. The remainder of this article describes the FRFT.

Introduction

l{F} of a function f: R \mapsto C is a unitary operator of L ^{2} space that maps the function f to its frequential version \hat{f} (all expressions are taken in the L ^{2} sense, rather than pointwise):

$$\backslash hat(\backslash xi)\; =\; \backslash int\_^\; f(x)\backslash \; e^\backslash ,\backslash mathrmx$$

and

f is determined by \hat{f} via the inverse transform l{F} ^{-1} ,

$$f(x)\; =\; \backslash int\_^\; \backslash hat(\backslash xi)\backslash \; e^\backslash ,\backslash mathrm\backslash xi\backslash ,\; .$$

l{F} ^{n} defined by l{F} ^{n} [f] = l{F}[ l{F} ^{n-1} [f]] and l{F} ^{-n} = ( l{F} ^{-1} ) ^{n} when n is a non-negative integer, and l{F} ^{0} [f] = f . Their sequence is finite since l{F} is a 4-periodic automorphism: for every function f , l{F} ^{4} [f] = f .

More precisely, let us introduce the parity operator

l{P} that inverts x , l{P}[f]\colon x \mapsto f( - x) . Then the following properties hold:$$\backslash mathcal^0\; =\; \backslash mathrm,\; \backslash qquad\; \backslash mathcal^1\; =\; \backslash mathcal,\; \backslash qquad\; \backslash mathcal^2\; =\; \backslash mathcal,\; \backslash qquad\; \backslash mathcal^4\; =\; \backslash mathrm$$$$\backslash mathcal^3\; =\; \backslash mathcal^\; =\; \backslash mathcal\; \backslash circ\; \backslash mathcal\; =\; \backslash mathcal\; \backslash circ\; \backslash mathcal.$$

The FRFT provides a family of linear transforms that further extends this definition to handle non-integer powers

n = 2 \alpha/\pi of the FT.

Definition

Note: some authors write the transform in terms of the "order " instead of the "angle ", in which case the is usually times . Although these two forms are equivalent, one must be careful about which definition the author uses.

For any real, the -angle fractional Fourier transform of a function ƒ is denoted by

l{F} _{\alpha} (u) and defined by

^{[8] [9] [10]}

For, this becomes precisely the definition of the continuous Fourier transform, and for it is the definition of the inverse continuous Fourier transform.

The FRFT argument is neither a spatial one nor a frequency . We will see why it can be interpreted as linear combination of both coordinates . When we want to distinguish the -angular fractional domain, we will let

x _{a} denote the argument of l{F} _{\alpha} .

Remark: with the angular frequency ω convention instead of the frequency one, the FRFT formula is the Mehler kernel,$$\backslash mathcal\_\backslash alpha(f)(\backslash omega)\; =\; \backslash sqrt\; e^\; \backslash int\_^\backslash infty\; e^f(t)\backslash ,\; dt~.$$

Properties

The -th order fractional Fourier transform operator,

l{F} _{\alpha} , has the properties:

Additivity

For any real angles,$$\backslash mathcal\_\; =\; \backslash mathcal\_\backslash alpha\; \backslash circ\; \backslash mathcal\_\backslash beta\; =\; \backslash mathcal\_\backslash beta\; \backslash circ\; \backslash mathcal\_\backslash alpha.$$

Linearity

$$\backslash mathcal\_\backslash alpha\; \backslash left\; [\backslash sum\backslash nolimits\_k\; b\_kf\_k(u)\; \backslash right\; ]=\backslash sum\backslash nolimits\_k\; b\_k\backslash mathcal\_\backslash alpha\; \backslash left\; [f\_k(u)\; \backslash right\; ]$$

Integer Orders

If is an integer multiple of

\pi / 2 , then:$$\backslash mathcal\_\backslash alpha\; =\; \backslash mathcal\_\; =\; \backslash mathcal^k\; =\; (\backslash mathcal)^k$$

Moreover, it has following relation

Inverse

$$(\backslash mathcal\_\backslash alpha)^=\backslash mathcal\_$$

Commutativity

$$\backslash mathcal\_\backslash mathcal\_=\backslash mathcal\_\backslash mathcal\_$$

Associativity

$$\backslash left\; (\backslash mathcal\_\backslash mathcal\_\; \backslash right)\backslash mathcal\_\; =\; \backslash mathcal\_\; \backslash left\; (\backslash mathcal\_\backslash mathcal\_\; \backslash right)$$

Unitarity

$$\backslash int\; f(t)g^*(t)dt=\backslash int\; f\_\backslash alpha(u)g\_\backslash alpha^*(u)du$$

Time Reversal

$$\backslash mathcal\_\backslash alpha\backslash mathcal=\backslash mathcal\backslash mathcal\_\backslash alpha$$$$\backslash mathcal\_\backslash alpha[f(-u)]=f\_\backslash alpha(-u)$$

Transform of a shifted function

Define the shift and the phase shift operators as follows:

Then

that is,

Transform of a scaled function

Define the scaling and chirp multiplication operators as follows:

Then,

Notice that the fractional Fourier transform of

f(u/M) cannot be expressed as a scaled version of f _{\alpha} (u) . Rather, the fractional Fourier transform of f(u/M) turns out to be a scaled and chirp modulated version of f _{\alpha'} (u) where \alpha ≠ \alpha' is a different order.^[11]

Fractional kernel

The FRFT is an integral transform$$\backslash mathcal\_\backslash alpha\; f\; (u)\; =\; \backslash int\; K\_\backslash alpha\; (u,\; x)\; f(x)\backslash ,\; \backslash mathrmx$$where the α-angle kernel is

Here again the special cases are consistent with the limit behavior when approaches a multiple of .

The FRFT has the same properties as its kernels :

• symmetry:

K _{\alpha~(u,} u') = K _{\alpha} ~(u', u)

• inverse:

K ^{-1} _{\alpha} (u, u') = K ^{*} _{\alpha} (u, u') = K _{-\alpha} (u', u)

• additivity:

K _{\alpha+\beta} (u,u') = \int K _{\alpha} (u, u'') K _{\beta} (u'', u') d u''.

Related transforms

There also exist related fractional generalizations of similar transforms such as the discrete Fourier transform.

• The discrete fractional Fourier transform is defined by Zeev Zalevsky. A quantum algorithm to implement a version of the discrete fractional Fourier transform in sub-polynomial time is described by Somma.^[12]
• The Fractional wavelet transform (FRWT) is a generalization of the classical wavelet transform in the fractional Fourier transform domains.^[13]
• The chirplet transform for a related generalization of the wavelet transform.

Generalizations

The Fourier transform is essentially bosonic; it works because it is consistent with the superposition principle and related interference patterns. There is also a fermionic Fourier transform.^[14] These have been generalized into a supersymmetric FRFT, and a supersymmetric Radon transform. There is also a fractional Radon transform, a symplectic FRFT, and a symplectic wavelet transform.^[15] Because quantum circuits are based on unitary operations, they are useful for computing integral transforms as the latter are unitary operators on a function space. A quantum circuit has been designed which implements the FRFT.^[16]

Interpretation

The usual interpretation of the Fourier transform is as a transformation of a time domain signal into a frequency domain signal. On the other hand, the interpretation of the inverse Fourier transform is as a transformation of a frequency domain signal into a time domain signal. Fractional Fourier transforms transform a signal (either in the time domain or frequency domain) into the domain between time and frequency: it is a rotation in the time–frequency domain. This perspective is generalized by the linear canonical transformation, which generalizes the fractional Fourier transform and allows linear transforms of the time–frequency domain other than rotation.

Take the figure below as an example. If the signal in the time domain is rectangular (as below), it becomes a sinc function in the frequency domain. But if one applies the fractional Fourier transform to the rectangular signal, the transformation output will be in the domain between time and frequency.

The fractional Fourier transform is a rotation operation on a time–frequency distribution. From the definition above, for α = 0, there will be no change after applying the fractional Fourier transform, while for α = π/2, the fractional Fourier transform becomes a plain Fourier transform, which rotates the time–frequency distribution with π/2. For other value of α, the fractional Fourier transform rotates the time–frequency distribution according to α. The following figure shows the results of the fractional Fourier transform with different values of α.

See also: Time–frequency analysis.

Application

Fractional Fourier transform can be used in time frequency analysis and DSP.^[17] It is useful to filter noise, but with the condition that it does not overlap with the desired signal in the time–frequency domain. Consider the following example. We cannot apply a filter directly to eliminate the noise, but with the help of the fractional Fourier transform, we can rotate the signal (including the desired signal and noise) first. We then apply a specific filter, which will allow only the desired signal to pass. Thus the noise will be removed completely. Then we use the fractional Fourier transform again to rotate the signal back and we can get the desired signal.

Thus, using just truncation in the time domain, or equivalently low-pass filters in the frequency domain, one can cut out any convex set in time–frequency space. In contrast, using time domain or frequency domain tools without a fractional Fourier transform would only allow cutting out rectangles parallel to the axes.

Fractional Fourier transforms also have applications in quantum physics. For example, they are used to formulate entropic uncertainty relations,^[18] in high-dimensional quantum key distribution schemes with single photons,^[19] and in observing spatial entanglement of photon pairs.^[20]

They are also useful in the design of optical systems and for optimizing holographic storage efficiency.^{[21] [22]}

See also

• Least-squares spectral analysis
• Fractional calculus
• Mehler kernel

Other time–frequency transforms:

• Linear canonical transformation
• Short-time Fourier transform
• Wavelet transform
• Chirplet transform
• Cone-shape distribution function
• Quadratic Fourier transform
• Chirp Z-transform

Bibliography

• Candan . C. . Kutay . M. A. . Ozaktas . H. M. . May 2000 . The discrete fractional Fourier transform . IEEE Transactions on Signal Processing . 48 . 5 . 1329–1337 . 10.1109/78.839980 . 2000ITSP...48.1329C . 11693/11130 . free.
• Book: Ding, Jian-Jiun . 2007 . Time frequency analysis and wavelet transform . Class notes . Department of Electrical Engineering, National Taiwan University (NTU) . Taipei, Taiwan.
• Lohmann . A. W. . 1993 . Image rotation, Wigner rotation and the fractional Fourier transform . J. Opt. Soc. Am. . A . 10 . 2181–2186. 10.1364/JOSAA.10.002181 . 1993JOSAA..10.2181L .
• Book: Ozaktas . Haldun M. . Zalevsky . Zeev . Kutay . M. Alper . 2001 . The Fractional Fourier Transform with Applications in Optics and Signal Processing . John Wiley & Sons . Series in Pure and Applied Optics . 978-0-471-96346-2 . Haldun Ozaktas.
• Pei . Soo-Chang . Ding . Jian-Jiun . 2001 . Relations between fractional operations and time–frequency distributions, and their applications . IEEE Trans. Signal Process. . 49 . 8 . 1638–1655. 10.1109/78.934134 . 2001ITSP...49.1638P .
• Saxena . Rajiv . Singh . Kulbir . Fractional Fourier transform: A novel tool for signal processing . J. Indian Inst. Sci. . January–February 2005 . 85 . 11–26 . https://web.archive.org/web/20110716112239/http://journal.library.iisc.ernet.in/vol200501/paper2/11.pdf . 16 July 2011 .

External links

• DiscreteTFDs -- software for computing the fractional Fourier transform and time–frequency distributions
• "Fractional Fourier Transform" by Enrique Zeleny, The Wolfram Demonstrations Project.
• Dr YangQuan Chen's FRFT (Fractional Fourier Transform) Webpages
• LTFAT - A free (GPL) Matlab / Octave toolbox Contains several version of the fractional Fourier transform .

Notes and References

1. Condon . Edward U. . 1937 . Immersion of the Fourier transform in a continuous group of functional transformations . . 23 . 3 . 158–164 . 10.1073/pnas.23.3.158 . 16588141 . Edward Condon. 1076889 . 1937PNAS...23..158C . free .
2. Namias . V. . 1980 . The fractional order Fourier transform and its application to quantum mechanics . IMA Journal of Applied Mathematics . 25 . 3 . 241–265 . 10.1093/imamat/25.3.241.
3. Wiener . N. . April 1929 . Hermitian Polynomials and Fourier Analysis . Journal of Mathematics and Physics . 8 . 1–4 . 70–73 . 10.1002/sapm19298170.
4. Almeida . Luís B. . 1994 . The fractional Fourier transform and time–frequency representations . IEEE Trans. Signal Process. . 42 . 11 . 3084–3091. 10.1109/78.330368 . 1994ITSP...42.3084A . 29757211 .
5. Tao . Ran . Deng . Bing . Zhang . Wei-Qiang . Wang . Yue . 2008 . Sampling and sampling rate conversion of band limited signals in the fractional Fourier transform domain . IEEE Transactions on Signal Processing . 56 . 1 . 158–171. 10.1109/TSP.2007.901666 . 2008ITSP...56..158T . 7001222 .
6. Bhandari . A. . Marziliano . P. . 2010 . Sampling and reconstruction of sparse signals in fractional Fourier domain . IEEE Signal Processing Letters . 17 . 3 . 221–224. 10.1109/LSP.2009.2035242 . 2010ISPL...17..221B . 10356/92280 . 11959415 . free .
7. Bailey . D. H. . Swarztrauber . P. N. . 1991 . The fractional Fourier transform and applications . . 33 . 3 . 389–404. 10.1137/1033097 . (Note that this article refers to the chirp-z transform variant, not the FRFT.)
8. Formally, this formula is only valid when the input function is in a sufficiently nice space (such as L¹ or Schwartz space), and is defined via a density argument in the general case.
9. Missbauer . Andreas . Gabor Frames and the Fractional Fourier Transform . 2012 . MSc . . 2018-11-03 . https://web.archive.org/web/20181103131426/https://www.univie.ac.at/nuhag-php/bibtex/open_files/13586_Missbauer_Diplom_final.pdf . 2018-11-03 . dead .
10. If is an integer multiple of π, then the cotangent and cosecant functions above diverge. This apparent divergence can be handled by taking the limit in the sense of tempered distributions, and leads to a Dirac delta function in the integrand. This approach is consistent with the intuition that, since l{F} ^{2(f)=f(-t)~,} ~~ l{F} _{\alpha} ~ (f) must be simply or for an even or odd multiple of respectively.
11. An elementary recipe, using the contangent function, and its (multi-valued) inverse, for \alpha' in terms of \alpha and M exists.
12. Somma . Rolando D. . 2016 . Quantum simulations of one dimensional quantum systems . Quantum Information and Computation . 16 . 1125–1168 . 1503.06319v2.
13. Shi . Jun . Zhang . NaiTong . Liu . Xiaoping . June 2012 . A novel fractional wavelet transform and its applications . Sci. China Inf. Sci. . 55 . 6 . 1270–1279 . 10.1007/s11432-011-4320-x. 3772011 .
14. De Bie . Hendrik . 1 September 2008 . Fourier transform and related integral transforms in superspace . Journal of Mathematical Analysis and Applications . 345 . 1 . 147–164 . 10.1016/j.jmaa.2008.03.047 . 0805.1918 . 2008JMAA..345..147D . 17066592 .
15. Fan . Hong-yi . Hu . Li-yun . Optical transformation from chirplet to fractional Fourier transformation kernel . 2009 . Journal of Modern Optics . 56 . 11 . 1227–1229 . 10.1080/09500340903033690 . 0902.1800 . 2009JMOp...56.1227F . 118463188 .
16. Klappenecker . Andreas . Roetteler . Martin . January 2002 . Engineering Functional Quantum Algorithms . Physical Review A . 67 . 1 . 010302 . 10.1103/PhysRevA.67.010302 . quant-ph/0208130 . 14501861 .
17. Sejdić . Ervin . Djurović . Igor . Stanković . LJubiša . June 2011 . Fractional Fourier transform as a signal processing tool: An overview of recent developments . Signal Processing . 91 . 6 . 1351–1369 . 10.1016/j.sigpro.2010.10.008. 14203403 .
18. Huang . Yichen . 24 May 2011 . Entropic uncertainty relations in multidimensional position and momentum spaces . Physical Review A . 83 . 5 . 052124 . 10.1103/PhysRevA.83.052124 . 119243096 . 1101.2944 . 2011PhRvA..83e2124H .
19. Walborn . SP . Lemelle . DS . Tasca . DS . Souto Ribeiro . PH . 13 June 2008 . Schemes for quantum key distribution with higher-order alphabets using single-photon fractional Fourier optics . Physical Review A . 77 . 6 . 062323 . 10.1103/PhysRevA.77.062323.
20. Tasca . DS . Walborn . SP . Souto Ribeiro . PH . Toscano. F . 8 July 2008 . Detection of transverse entanglement in phase space . Physical Review A . 78 . 1 . 010304(R) . 10.1103/PhysRevA.78.010304 . 0806.3044 . 118607762 .
21. Pégard . Nicolas C. . Fleischer . Jason W. . 2011 . Optimizing holographic data storage using a fractional Fourier transform . Optics Letters . 36 . 13 . 2551–2553 . 10.1364/OL.36.002551 . 21725476 . 2011OptL...36.2551P .
22. Jagoszewski . Eugeniusz . 1998 . Fractional Fourier transform in optical setups . Optica Applicata. XXVIII . 3 . 227-237.