- Trending Categories
Data Structure
Networking
RDBMS
Operating System
Java
MS Excel
iOS
HTML
CSS
Android
Python
C Programming
C++
C#
MongoDB
MySQL
Javascript
PHP
Physics
Chemistry
Biology
Mathematics
English
Economics
Psychology
Social Studies
Fashion Studies
Legal Studies
- Selected Reading
- UPSC IAS Exams Notes
- Developer's Best Practices
- Questions and Answers
- Effective Resume Writing
- HR Interview Questions
- Computer Glossary
- Who is Who
Fourier Transform of Rectangular Function
Fourier Transform
The Fourier transform of a continuous-time function $x(t)$ can be defined as,
$$\mathrm{X(\omega)=\int_{−\infty}^{\infty}x(t)e^{-j\omega t}\:dt}$$
Fourier Transform of Rectangular Function
Consider a rectangular function as shown in Figure-1.
It is defined as,
$$\mathrm{rect\left(\frac{t}{τ}\right)=\prod\left(\frac{t}{τ}\right)=\begin{cases}1 & for\:|t|≤ \left(\frac{τ}{2}\right)\0 & otherwise\end{cases}}$$
Given that
$$\mathrm{x(t)=\prod\left(\frac{t}{τ}\right)}$$
Hence, from the definition of Fourier transform, we have,
$$\mathrm{F\left[\prod\left(\frac{t}{τ}\right) \right]=X(\omega)=\int_{−\infty}^{\infty}x(t)e^{-j\omega t}\:dt=\int_{−\infty}^{\infty}\prod\left(\frac{t}{τ}\right)e^{-j\omega t}\:dt}$$
$$\mathrm{\Rightarrow\:X(\omega)=\int_{−(τ/2)}^{(τ/2)}1\cdot e^{-j\omega t}\:dt=\left[\frac{e^{-j\omega t}}{-j\omega} \right]_{-τ/2}^{τ/2}}$$
$$\mathrm{\Rightarrow\:X(\omega)=\left[ \frac{e^{-j\omega (τ/2)}-e^{j\omega (τ/2)}}{-j\omega}\right]=\left[ \frac{e^{j\omega (τ/2)}-e^{-j\omega (τ/2)}}{j\omega }\right]}$$
$$\mathrm{\Rightarrow\:X(\omega)=\left[ \frac{2τ[e^{j\omega (τ/2)}-e^{-j\omega (τ/2)}]}{j\omega\cdot (2τ) }\right]=\frac{τ}{\omega(τ/2)}\left[\frac{e^{j\omega (τ/2)}-e^{-j\omega (τ/2)}}{2j} \right]}$$
$$\mathrm{\because \:\left[\frac{e^{j\omega (τ/2)}-e^{-j\omega (τ/2)}}{2j} \right]=sin\:\omega (τ/2)}$$
$$\mathrm{\therefore\:X(\omega)=\frac{τ}{\omega(τ/2)}\cdot sin \omega (τ/2)=τ \left[\frac{sin\omega (τ/2)}{\omega (τ/2)}\right]}$$
$$\mathrm{\because\:sinc \left(\frac{\omega τ}{2}\right)=\frac{sin\omega (τ/2)}{\omega (τ/2)}}$$
$$\mathrm{\therefore\:X(\omega)=τ\cdot sinc \left(\frac{\omega τ}{2}\right)}$$
Therefore, the Fourier transform of the rectangular function is
$$\mathrm{F\left[\prod\left(\frac{t}{τ}\right)\right]=τ\cdot sinc \left(\frac{\omega τ}{2}\right)}$$
Or, it can also be represented as,
$$\mathrm{\prod\left(\frac{t}{τ}\right) \overset{FT}{\leftrightarrow}τ\cdot sinc \left(\frac{\omega τ}{2}\right)}$$
Magnitude and phase spectrum of Fourier transform of the rectangular function
The magnitude spectrum of the rectangular function is obtained as −
At $\omega=0$:
$$\mathrm{sinc\left(\frac{\omega τ}{2}\right)=1;\:\:\Rightarrow|X(\omega)|=τ}$$
At $\left(\frac{\omega τ}{2}\right)=± n\pi$ i.e., at
$$\mathrm{\omega=±\frac{2n\pi}{τ},\:\:n=1,2,2,3,...}$$
$$\mathrm{sinc\left(\frac{\omega τ}{2}\right)=0}$$
The phase spectrum is obtained as −
$$\mathrm{\angle\:X(\omega)=\begin{cases}0 & if\:sinc\:\left(\frac{\omega τ}{2}\right)>0\±\pi & if\:sinc\:\left(\frac{\omega τ}{2}\right)<0 \end{cases}}$$
The frequency spectrum of the rectangular function is shown in Figure-2.
Note
The magnitude response between the first two zero crossings is known as the main lobe.
The portions of the magnitude response for $\omega\:< -\left( \frac{-2\pi}{τ}\right)$ and $\omega > \left( \frac{2\pi}{τ}\right)$are known as the side lobes.
From the magnitude spectrum, it is clear that the majority of the energy of the signal is contained in the main lobe.
The main lobe becomes narrower with the increase in the width of the rectangular pulse.
The phase spectrum of the rectangular function is an odd function of the frequency (ω).
When the magnitude spectrum is positive, then the phase is zero and if the magnitude spectrum is negative, then the phase is $(±\pi)$.
- Related Articles
- Fourier Transform of Signum Function
- Fourier Transform of Unit Step Function
- Derivation of Fourier Transform from Fourier Series
- Fourier Transform of Unit Impulse Function, Constant Amplitude and Complex Exponential Function
- Modulation Property of Fourier Transform
- Difference between Fourier Series and Fourier Transform
- Discrete-Time Fourier Transform
- Difference between Laplace Transform and Fourier Transform
- Relation between Laplace Transform and Fourier Transform
- Frequency Derivative Property of Fourier Transform
- Time Differentiation Property of Fourier Transform
- Fourier Transform of a Triangular Pulse
- Time Scaling Property of Fourier Transform
- Fourier Transform of a Gaussian Signal
- Inverse Discrete-Time Fourier Transform
