Connexions

You are here: Home » Content » The Impulse Function
Content Actions
Lenses

What is a lens?

Lenses

A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

What is in a lens?

Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

Who can create a lens?

Any individual Connexions member, a community, or a respected organization.

This content is ...
Affiliated with (?)
This content is either by members of the organizations listed or about topics related to the organizations listed. Click each link to see a list of all content affiliated with the organization.

  • This module is included inLens: Rice University OpenCourseWare
    By: OpenCourseWare ConsortiumAs a part of collection:"Signals and Systems"

    Click the "Rice University OCW" link to see all content affiliated with them.

    Rice University OCW
Also in these lenses

  • This module is included inLens: richb's DSP resources
    By: Richard BaraniukAs a part of collection:"Signals and Systems"

    Comments:

    "My introduction to signal processing course at Rice University."

    Click the "richb's DSP" link to see all content selected in this lens.

    richb's DSP
Tags

(?)

These tags come from the endorsement, affiliation, and other lenses that include this content.

The Impulse Function

Module by: Melissa Selik, Richard Baraniuk

Summary: Explains the use of the unit impulse function.

In engineering, we often deal with the idea of an action occurring at a point. Whether it be a force at a point in space or a signal at a point in time, it becomes worth while to develop some way of quantitatively defining this. This leads us to the idea of a unit impulse, probably the second most important function, next to the complex exponential, in systems and signals course.

Dirac Delta Function

The Dirac Delta function, often referred to as the unit impulse or delta function, is the function that defines the idea of a unit impulse. This function is one that is infinitesimally narrow, infinitely tall, yet integrates to unity, one (see Equation 1 below). Perhaps the simplest way to visualize this is as a rectangular pulse from a-ε2 a ε 2 to a+ε2 a ε 2 with a height of 1ε 1 ε . As we take the limit of this, limε→00 ε 0 , we see that the width tends to zero and the height tends to infinity as the total area remains constant at one. The impulse function is often written as δt δt .
∫-∞∞δtdt= 1 t δ t 1 (1)
impulsefunc1.png
Figure 1: This is one way to visualize the Dirac Delta Function.
impulsefunc2.png
Figure 2: Since it is quite difficult to draw something that is infinitely tall, we represent the Dirac with an arrow centered at the point it is applied. If we wish to scale it, we may write the value it is scaled by next to the point of the arrow. This is a unit impulse (no scaling).

Download LabVIEW Source

The Sifting Property of the Impulse

The first step to understanding what this unit impulse function gives us is to examine what happens when we multiply another function by it.
ftδt=f0δt f t δ t f 0 δ t (2)
Since the impulse function is zero everywhere except the origin, we essentially just "pick off" the value of the function we are multiplying by evaluated at zero.
At first glance this may not appear to give use much, since we already know that the impulse evaluated at zero is infinity, and anything times infinity is infinity. However, what happens if we integrate this?
Sifting Property ∫-∞∞ftδtdt=∫-∞∞f0δtdt=f0∫-∞∞δtdt=f0 t f t δ t t f 0 δ t f 0 t δ t f 0 (3)
It quickly becomes apparent that what we end up with is simply the function evaluated at zero. Had we used δt-T δ t T instead of δt δ t , we could have "sifted out" fT f T . This is what we call the Sifting Property of the Dirac function, which is often used to define the unit impulse.
The Sifting Property is very useful in developing the idea of convolution which is one of the fundamental principles of signal processing. By using convolution and the sifting property we can represent an approximation of any system's output if we know the system's impulse response and input. Click on the convolution link above for more information on this.

Other Impulse Properties

Below we will briefly list a few of the other properties of the unit impulse without going into detail of their proofs - we will leave that up to you to verify as most are straightforward. Note that these properties hold for continuous and discrete time.
    Unit Impulse Properties
  • δαt=1|α|δt δ α t 1 α δ t
  • δt=δ-t δ t δ t
  • δt=ddtut δ t t u t , where ut u t is the unit step.

Discrete-Time Impulse (Unit Sample)

The extension of the Unit Impulse Function to discrete-time becomes quite trivial. All we really need to realize is that integration in continuous-time equates to summation in discrete-time. Therefore, we are looking for a signal that sums to zero and is zero everywhere except at zero.
Discrete-Time Impulse δn=1ifn=00otherwise δ n 1 n 0 0 (4)
impulsefunc3.png
Figure 3: The graphical representation of the discrete-time impulse function
Looking at the discrete-time plot of any discrete signal one can notice that all discrete signals are composed of a set of scaled, time-shifted unit samples. If we let the value of a sequence at each integer k k be denoted by sk sk and the unit sample delayed that occurs at k k to be written as δn-k δ nk , we can write any signal as the sum of delayed unit samples that are scaled by the signal value, or weighted coefficients.
sn=∑k=-∞∞skδn-k sn k sk δ nk (5)
This decomposition is strictly a property of discrete-time signals and proves to be a very useful property.
note: Through the above reasoning, we have formed Equation 5, which is the fundamental concept of discrete-time convolution.

The Impulse Response

The impulse response is exactly what its name implies - the response of an LTI system, such as a filter, when the system's input is the unit impulse (or unit sample). A system can be completed described by its impulse response due to the idea mentioned above that all signals can be represented by a superposition of signals. An impulse response gives an equivalent description of a system as a transfer function, since they are Laplace Transforms of each other.
notation: Most texts use δtδt and δnδn to denote the continuous-time and discrete-time impulse response, respectively.

Comments, questions, feedback, criticisms?

Send feedback