Dirac comb

Last updated
The graph of the Dirac comb function is an infinite series of Dirac delta functions spaced at intervals of T Dirac comb.svg
The graph of the Dirac comb function is an infinite series of Dirac delta functions spaced at intervals of T

In mathematics, a Dirac comb (also known as shah function, impulse train or sampling function) is a periodic function with the formula

Contents

for some given period . [1] Here t is a real variable and the sum extends over all integers k. The Dirac delta function and the Dirac comb are tempered distributions. [2] [3] The graph of the function resembles a comb (with the s as the comb's teeth), hence its name and the use of the comb-like Cyrillic letter sha (Ш) to denote the function.

The symbol , where the period is omitted, represents a Dirac comb of unit period. This implies [1]

Because the Dirac comb function is periodic, it can be represented as a Fourier series based on the Dirichlet kernel: [1]

The Dirac comb function allows one to represent both continuous and discrete phenomena, such as sampling and aliasing, in a single framework of continuous Fourier analysis on tempered distributions, without any reference to Fourier series. The Fourier transform of a Dirac comb is another Dirac comb. Owing to the Convolution Theorem on tempered distributions which turns out to be the Poisson summation formula, in signal processing, the Dirac comb allows modelling sampling by multiplication with it, but it also allows modelling periodization by convolution with it. [4]

Dirac-comb identity

The Dirac comb can be constructed in two ways, either by using the comb operator (performing sampling) applied to the function that is constantly , or, alternatively, by using the rep operator (performing periodization) applied to the Dirac delta . Formally, this yields (Woodward 1953; Brandwood 2003)

where

and

In signal processing, this property on one hand allows sampling a function by multiplication with , and on the other hand it also allows the periodization of by convolution with (Bracewell 1986). The Dirac comb identity is a particular case of the Convolution Theorem for tempered distributions.

Scaling

The scaling property of the Dirac comb follows from the properties of the Dirac delta function. Since [5] for positive real numbers , it follows that:

Note that requiring positive scaling numbers instead of negative ones is not a restriction because the negative sign would only reverse the order of the summation within , which does not affect the result.

Fourier series

It is clear that is periodic with period . That is,

for all t. The complex Fourier series for such a periodic function is

where the Fourier coefficients are (symbolically)

All Fourier coefficients are 1/T resulting in

When the period is one unit, this simplifies to

Remark: Most rigorously, Riemann or Lebesgue integration over any products including a Dirac delta function yields zero. For this reason, the integration above (Fourier series coefficients determination) must be understood "in the generalized functions sense". It means that, instead of using the characteristic function of an interval applied to the Dirac comb, one uses a so-called Lighthill unitary function as cutout function, see Lighthill 1958, p.62, Theorem 22 for details.

Fourier transform

The Fourier transform of a Dirac comb is also a Dirac comb. For the Fourier transform expressed in frequency domain (Hz) the Dirac comb of period transforms into a rescaled Dirac comb of period i.e. for

is proportional to another Dirac comb, but with period in frequency domain (radian/s). The Dirac comb of unit period is thus an eigenfunction of to the eigenvalue

This result can be established (Bracewell 1986) by considering the respective Fourier transforms of the family of functions defined by

Since is a convergent series of Gaussian functions, and Gaussians transform into Gaussians, each of their respective Fourier transforms also results in a series of Gaussians, and explicit calculation establishes that

The functions and are thus each resembling a periodic function consisting of a series of equidistant Gaussian spikes and whose respective "heights" (pre-factors) are determined by slowly decreasing Gaussian envelope functions which drop to zero at infinity. Note that in the limit each Gaussian spike becomes an infinitely sharp Dirac impulse centered respectively at and for each respective and , and hence also all pre-factors in eventually become indistinguishable from . Therefore the functions and their respective Fourier transforms converge to the same function and this limit function is a series of infinite equidistant Gaussian spikes, each spike being multiplied by the same pre-factor of one, i.e. the Dirac comb for unit period:

and

Since , we obtain in this limit the result to be demonstrated:

The corresponding result for period can be found by exploiting the scaling property of the Fourier transform,

Another manner to establish that the Dirac comb transforms into another Dirac comb starts by examining continuous Fourier transforms of periodic functions in general, and then specialises to the case of the Dirac comb. In order to also show that the specific rule depends on the convention for the Fourier transform, this will be shown using angular frequency with for any periodic function its Fourier transform

obeys:

because Fourier transforming and leads to and This equation implies that nearly everywhere with the only possible exceptions lying at with and When evaluating the Fourier transform at the corresponding Fourier series expression times a corresponding delta function results. For the special case of the Fourier transform of the Dirac comb, the Fourier series integral over a single period covers only the Dirac function at the origin and thus gives for each This can be summarised by interpreting the Dirac comb as a limit of the Dirichlet kernel such that, at the positions all exponentials in the sum point into the same direction and add constructively. In other words, the continuous Fourier transform of periodic functions leads to

with

and

The Fourier series coefficients for all when , i.e.

is another Dirac comb, but with period in angular frequency domain (radian/s).

As mentioned, the specific rule depends on the convention for the used Fourier transform. Indeed, when using the scaling property of the Dirac delta function, the above may be re-expressed in ordinary frequency domain (Hz) and one obtains again:

such that the unit period Dirac comb transforms to itself:

Finally, the Dirac comb is also an eigenfunction of the unitary continuous Fourier transform in angular frequency space to the eigenvalue 1 when because for the unitary Fourier transform

the above may be re-expressed as

Sampling and aliasing

Multiplying any function by a Dirac comb transforms it into a train of impulses with integrals equal to the value of the function at the nodes of the comb. This operation is frequently used to represent sampling.

Due to the self-transforming property of the Dirac comb and the convolution theorem, this corresponds to convolution with the Dirac comb in the frequency domain.

Since convolution with a delta function is equivalent to shifting the function by , convolution with the Dirac comb corresponds to replication or periodic summation:

This leads to a natural formulation of the Nyquist–Shannon sampling theorem. If the spectrum of the function contains no frequencies higher than B (i.e., its spectrum is nonzero only in the interval ) then samples of the original function at intervals are sufficient to reconstruct the original signal. It suffices to multiply the spectrum of the sampled function by a suitable rectangle function, which is equivalent to applying a brick-wall lowpass filter.

In time domain, this "multiplication with the rect function" is equivalent to "convolution with the sinc function" (Woodward 1953, p.33-34). Hence, it restores the original function from its samples. This is known as the Whittaker–Shannon interpolation formula.

Remark: Most rigorously, multiplication of the rect function with a generalized function, such as the Dirac comb, fails. This is due to undetermined outcomes of the multiplication product at the interval boundaries. As a workaround, one uses a Lighthill unitary function instead of the rect function. It is smooth at the interval boundaries, hence it yields determined multiplication products everywhere, see Lighthill 1958, p.62, Theorem 22 for details.

Use in directional statistics

In directional statistics, the Dirac comb of period is equivalent to a wrapped Dirac delta function and is the analog of the Dirac delta function in linear statistics.

In linear statistics, the random variable is usually distributed over the real-number line, or some subset thereof, and the probability density of is a function whose domain is the set of real numbers, and whose integral from to is unity. In directional statistics, the random variable is distributed over the unit circle, and the probability density of is a function whose domain is some interval of the real numbers of length and whose integral over that interval is unity. Just as the integral of the product of a Dirac delta function with an arbitrary function over the real-number line yields the value of that function at zero, so the integral of the product of a Dirac comb of period with an arbitrary function of period over the unit circle yields the value of that function at zero.

See also

Related Research Articles

<span class="mw-page-title-main">Dirac delta function</span> Generalized function whose value is zero everywhere except at zero

In mathematics, the Dirac delta distribution, also known as the unit impulse, is a generalized function or distribution over the real numbers, whose value is zero everywhere except at zero, and whose integral over the entire real line is equal to one.

<span class="mw-page-title-main">Fourier transform</span> Mathematical transform that expresses a function of time as a function of frequency

A Fourier transform (FT) is a mathematical transform that decomposes functions into frequency components, which are represented by the output of the transform as a function of frequency. Most commonly functions of time or space are transformed, which will output a function depending on temporal frequency or spatial frequency respectively. That process is also called analysis. An example application would be decomposing the waveform of a musical chord into terms of the intensity of its constituent pitches. The term Fourier transform refers to both the frequency domain representation and the mathematical operation that associates the frequency domain representation to a function of space or time.

In mathematics, the convolution theorem states that under suitable conditions the Fourier transform of a convolution of two functions is the pointwise product of their Fourier transforms. More generally, convolution in one domain equals point-wise multiplication in the other domain. Other versions of the convolution theorem are applicable to various Fourier-related transforms.

<span class="mw-page-title-main">Heaviside step function</span> Indicator function of positive numbers

The Heaviside step function, or the unit step function, usually denoted by H or θ, is a step function, named after Oliver Heaviside (1850–1925), the value of which is zero for negative arguments and one for positive arguments. It is an example of the general class of step functions, all of which can be represented as linear combinations of translations of this one.

<span class="mw-page-title-main">Spectral density</span> Relative importance of certain frequencies in a composite signal

The power spectrum of a time series describes the distribution of power into frequency components composing that signal. According to Fourier analysis, any physical signal can be decomposed into a number of discrete frequencies, or a spectrum of frequencies over a continuous range. The statistical average of a certain signal or sort of signal as analyzed in terms of its frequency content, is called its spectrum.

<span class="mw-page-title-main">Short-time Fourier transform</span> Fourier-related transform suited to signals that change rather quickly in time

The short-time Fourier transform (STFT), is a Fourier-related transform used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. In practice, the procedure for computing STFTs is to divide a longer time signal into shorter segments of equal length and then compute the Fourier transform separately on each shorter segment. This reveals the Fourier spectrum on each shorter segment. One then usually plots the changing spectra as a function of time, known as a spectrogram or waterfall plot, such as commonly used in software defined radio (SDR) based spectrum displays. Full bandwidth displays covering the whole range of an SDR commonly use fast Fourier transforms (FFTs) with 2^24 points on desktop computers.

In signal processing, a finite impulse response (FIR) filter is a filter whose impulse response is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely.

In mathematics, the Poisson summation formula is an equation that relates the Fourier series coefficients of the periodic summation of a function to values of the function's continuous Fourier transform. Consequently, the periodic summation of a function is completely defined by discrete samples of the original function's Fourier transform. And conversely, the periodic summation of a function's Fourier transform is completely defined by discrete samples of the original function. The Poisson summation formula was discovered by Siméon Denis Poisson and is sometimes called Poisson resummation.

In mathematics and in signal processing, the Hilbert transform is a specific linear operator that takes a function, u(t) of a real variable and produces another function of a real variable H(u)(t). This linear operator is given by convolution with the function . The Hilbert transform has a particularly simple representation in the frequency domain: It imparts a phase shift of ±90° to every frequency component of a function, the sign of the shift depending on the sign of the frequency. The Hilbert transform is important in signal processing, where it is a component of the analytic representation of a real-valued signal u(t). The Hilbert transform was first introduced by David Hilbert in this setting, to solve a special case of the Riemann–Hilbert problem for analytic functions.

In mathematics, the discrete-time Fourier transform (DTFT) is a form of Fourier analysis that is applicable to a sequence of values.

The Havriliak–Negami relaxation is an empirical modification of the Debye relaxation model in electromagnetism. Unlike the Debye model, the Havriliak–Negami relaxation accounts for the asymmetry and broadness of the dielectric dispersion curve. The model was first used to describe the dielectric relaxation of some polymers, by adding two exponential parameters to the Debye equation:

A cyclostationary process is a signal having statistical properties that vary cyclically with time. A cyclostationary process can be viewed as multiple interleaved stationary processes. For example, the maximum daily temperature in New York City can be modeled as a cyclostationary process: the maximum temperature on July 21 is statistically different from the temperature on December 20; however, it is a reasonable approximation that the temperature on December 20 of different years has identical statistics. Thus, we can view the random process composed of daily maximum temperatures as 365 interleaved stationary processes, each of which takes on a new value once per year.

In applied mathematics, the Wiener–Khinchin theorem or Wiener–Khintchine theorem, also known as the Wiener–Khinchin–Einstein theorem or the Khinchin–Kolmogorov theorem, states that the autocorrelation function of a wide-sense-stationary random process has a spectral decomposition given by the power spectrum of that process.

<span class="mw-page-title-main">Wigner distribution function</span>

The Wigner distribution function (WDF) is used in signal processing as a transform in time-frequency analysis.

<span class="mw-page-title-main">Gabor transform</span>

The Gabor transform, named after Dennis Gabor, is a special case of the short-time Fourier transform. It is used to determine the sinusoidal frequency and phase content of local sections of a signal as it changes over time. The function to be transformed is first multiplied by a Gaussian function, which can be regarded as a window function, and the resulting function is then transformed with a Fourier transform to derive the time-frequency analysis. The window function means that the signal near the time being analyzed will have higher weight. The Gabor transform of a signal x(t) is defined by this formula:

In mathematics, a local martingale is a type of stochastic process, satisfying the localized version of the martingale property. Every martingale is a local martingale; every bounded local martingale is a martingale; in particular, every local martingale that is bounded from below is a supermartingale, and every local martingale that is bounded from above is a submartingale; however, in general a local martingale is not a martingale, because its expectation can be distorted by large values of small probability. In particular, a driftless diffusion process is a local martingale, but not necessarily a martingale.

In many-body theory, the term Green's function is sometimes used interchangeably with correlation function, but refers specifically to correlators of field operators or creation and annihilation operators.

Bilinear time–frequency distributions, or quadratic time–frequency distributions, arise in a sub-field of signal analysis and signal processing called time–frequency signal processing, and, in the statistical analysis of time series data. Such methods are used where one needs to deal with a situation where the frequency composition of a signal may be changing over time; this sub-field used to be called time–frequency signal analysis, and is now more often called time–frequency signal processing due to the progress in using these methods to a wide range of signal-processing problems.

<span class="mw-page-title-main">Dirichlet kernel</span>

In mathematical analysis, the Dirichlet kernel, named after the German mathematician Peter Gustav Lejeune Dirichlet, is the collection of periodic functions defined as

In thermal quantum field theory, the Matsubara frequency summation is the summation over discrete imaginary frequencies. It takes the following form

References

  1. 1 2 3 "The Dirac Comb and its Fourier Transform - DSPIllustrations.com". dspillustrations.com. Retrieved 2022-06-28.
  2. Schwartz, L. (1951), Théorie des distributions, vol. Tome I, Tome II, Hermann, Paris
  3. Strichartz, R. (1994), A Guide to Distribution Theory and Fourier Transforms, CRC Press, ISBN   0-8493-8273-4
  4. Bracewell, R. N. (1986), The Fourier Transform and Its Applications (revised ed.), McGraw-Hill; 1st ed. 1965, 2nd ed. 1978.
  5. Rahman, M. (2011), Applications of Fourier Transforms to Generalized Functions, WIT Press Southampton, Boston, ISBN   978-1-84564-564-9 .

Further reading