What Is the Fourier Transform of a Constant?

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Discussion Overview

The discussion revolves around the Fourier transform of a constant function, specifically addressing whether it is represented as δ(f) or cδ(f), where δ denotes the Dirac delta function. Participants explore mathematical proofs, properties of the Dirac delta function, and the implications of these representations.

Discussion Character

  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • Some participants assert that the Fourier transform of a constant is δ(f), while others clarify that it should be cδ(f).
  • One participant attempts to prove the relationship but expresses uncertainty about the correctness of their approach.
  • Another participant acknowledges the confusion regarding the Dirac delta function's properties, specifically its evenness, stating δ(f) = δ(-f).
  • A later post introduces various properties of the Dirac delta function, including its even function nature and the masking property, as part of a broader explanation of how to derive the Fourier transform of a constant.
  • Participants discuss the implications of the duality and linearity theorems in the context of the Fourier transform.

Areas of Agreement / Disagreement

There is no consensus on the exact representation of the Fourier transform of a constant, with multiple competing views presented. Some participants agree on the properties of the Dirac delta function, but the main question remains unresolved.

Contextual Notes

Participants express uncertainty about the mathematical steps involved in proving the Fourier transform of a constant. The discussion includes references to properties of the Dirac delta function that may depend on specific definitions and interpretations.

Somali_Physicist
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It is often reported that the Fourier transform of a constant is δ(f) : that δ denotes the dirac delta function.

ƒ{c} = δ(f) : c ∈ R & f => Fourier transform
however i cannot prove this
Here is my attempt:(assume integrals are limits to [-∞,∞])
ƒ{c} = ∫ce-2πftdt = c∫e-2πftdt = c∫ƒ{δ(f)}e-2πftdt
=> cf{f{δ(f)}} => cδ(-f)

this is incorrect, any help would be appreciated thank you.
 
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Somali_Physicist said:
It is often reported that the Fourier transform of a constant is δ(f) : that δ denotes the dirac delta function.
What you just saying above is not exactly correct, the Fourier transform of a constant is ##c\delta(f)##.

ƒ{c} = δ(f) : c ∈ R & f => Fourier transform
however i cannot prove this
Here is my attempt:(assume integrals are limits to [-∞,∞])
ƒ{c} = ∫ce-2πftdt = c∫e-2πftdt = c∫ƒ{δ(f)}e-2πftdt
=> cf{f{δ(f)}} => cδ(-f)

this is incorrect, any help would be appreciated thank you.

What you do is unnecessary but it is correct, as I said above the Fourier transform of a constant c is ##c\delta(f)=c\delta(-f)##.
 
Delta2 said:
What you just saying above is not exactly correct, the Fourier transform of a constant is ##c\delta(f)##.
What you do is unnecessary but it is correct, as I said above the Fourier transform of a constant c is ##c\delta(f)=c\delta(-f)##.
Ahh yes i forgot about the constant, what was confusing is that
δ(f)=δ(-f)

is there a proof for that?

EDIT:
oh wait if the "inifinite" jump for this dirac delta function is at 0 then its negative is essentially its positive.Thanks dude :)
 
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Somali_Physicist said:
It is often reported that the Fourier transform of a constant is δ(f) : that δ denotes the dirac delta function..
Hello.
Not just reported, but we can prove it right here and now, first I would like to introduce you to the properties of the pulse/distribution.

$$ \textbf{The dirac delta pulse and its mathematical properties} $$
The definition of the pulse in continuous time is:
$$ \delta(t) = \begin{cases} \infty & \text{ $$ t = 0 $$} \\
0 & \text{ Otherwise } \end{cases} $$
$$ 1. \,\,\,\, \text{It is an even function} $$
Definition of an even function: ## f(t) = f(-t) ##.
Definition of an odd function: ## -f(t) = f(-t) ##
hence:
$$ \delta(t) = \delta(-t) $$
$$ 2. \,\,\,\, \text{It has a time scaling property} $$
$$ \delta(at) = \dfrac{1}{|a|} \cdot \delta(t) \,\,\,\,\,\, a \ne 0$$

$$ 3. \,\,\,\, \text{It has a unit area property} $$
$$\displaystyle \int_{a}^{b} \delta(t \pm t_{0} ) \,\,\,\, \text{dt} = \begin{cases} 1 & \text{ $\mp t_{0} \in [a,b] $} \\ 0 & \text{ $ \mp t_{0} \not\in [a,b] $} \end{cases} $$
$$ 4. \,\,\,\, \text{It has a masking property} $$

This is an important property used in LTI systems and convolution, basically, because of the masking property all functions can be expressed as a sum of scaled and shifted dirac pulses.
$$ \displaystyle \int_{-\infty}^{\infty} f(t) \cdot \delta(t \pm t_{0}) = f(\mp t_{0} ) $$
Masking without an integral:
$$
f(t) \delta(t \mp t_{0} ) = f(\pm t_{0}) \delta(t \mp t_{0} )
$$
Example:
$$ cos(x) \delta(x) = cos(0) \delta(x) = \delta(x) $$

There are other properties, like the connection to the unit step function, among others.
Anyway, the Fourier transform of a constant can be derived using the:

$$ \textbf{The masking property and the duality and linearity theorem of the Fourier transform } $$
Duality theorem of the Fourier transform: If the function ##f(t)## has a Fourier trasnform of ## X(j \omega) ## then the function ## X(t) ## would have a Fourier transform of ## 2 \pi f(-j \omega) ##.

We first use the masking property to define the Fourier transform of the pulse as:
$$ \mathcal F \big( \delta(t) \big) = \displaystyle \int_{-\infty}^{\infty} \delta(t) e^{-j(\omega t) } \,\,\,\,\, \text{dt} = e^{j(0)} = 1 $$
Using the duality property property:
$$
1 \iff 2 \pi \delta(- \omega) = 2 \pi \delta(\omega) $$
Using the linearity theorem:
$$ k \cdot 1 \iff k 2 \pi \delta(\omega) $$But that's it, we use the masking property and duality and linearity theorems of the Fourier transform to find the Fourier transform for constants. Many of the properties of the pulse can be simply proved by the unit area property.
I don't know how to get the laplace and Fourier transform bars here, so if anyone knows, let me know.
 
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