Signal Analysis - Using Convolution

In summary, the problem is to find the value of T that makes the output of a system with impulse response h(t) equal to Acos(4t)+Bcos(5t) when x(t)=cos(4t)+cos(5t)+cos(6t). This can be solved using either the convolution method or the convolution theorem. The resulting equation can be simplified using the sum-to-product identity for sine, and the correct value of T is \pi/3 according to the solution.
  • #1
cathode-ray
50
0

Homework Statement



Consider the signal [tex]x(t)=cos(4t)+cos(5t)+cos(6t)[/tex], and the SLIT with impulse response:


[tex]h(t)=\begin{cases} 1, & \mbox{if } |t|<T \\ 0, & \mbox{if } |t|>T \end{cases}[/tex]

For what value of T is the output of the system [tex] y(t) [/tex] equal to [tex]Acos(4t)+Bcos(5t)[/tex], when [tex]x(t)[/tex] is the input?

The Attempt at a Solution


I know that this can be solved through Fourier Transform and the solution is [tex]T=\pi /3 [/tex]. My problem is that I tried to do this using the convolution, but it gaves me a sum of [tex]sin[/tex] and i don't know how to progress to solve by that way, or if it is possible to do it.
 
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  • #2
How were you planning to solve it using convolution directly? You'd have to compute the integral of h(s)*x(t-s)*ds, but first you'd have to express h(s)*x(t-s) in terms of analytical functions that can be easily integrated. One way to do this would be to use the Fourier transform, but if you're going to do that, you might as well use the convolution theorem.
 
  • #3
I was thinking to do it with the convolution directly:

[tex]\intop_{-\infty}^{+\infty}x(\tau)h(t-\tau)d\tau=\intop_{-T+t}^{T+t}x(\tau)h(t-\tau)d\tau=\intop_{-T+t}^{T+t}[cos(4\tau)+cos(5\tau)+cos(6\tau)]d\tau[/tex].

Integrating I get a very complicated expression with a sum of sin. I don't know how to progress further at this point.
 
  • #4
cathode-ray said:
I was thinking to do it with the convolution directly:

[tex]\intop_{-\infty}^{+\infty}x(\tau)h(t-\tau)d\tau=\intop_{-T+t}^{T+t}x(\tau)h(t-\tau)d\tau=\intop_{-T+t}^{T+t}[cos(4\tau)+cos(5\tau)+cos(6\tau)]d\tau[/tex].

Integrating I get a very complicated expression with a sum of sin. I don't know how to progress further at this point.

Yup, that should work. (Ignore what I said in the previous post; I was just being stupid.)

Integrating that should give you 1/4*(sin(4*(T+t)) - sin(4*t-T)) plus 2 other terms. You can use the sum-to-product identity:

sin u − sin v = 2 sin(½(u−v)) cos(½(u+v))

to simplify each term into a cosine factor that depends on t, and a sine factor that depends on T.

Using both this method and the convolution theorem, I got T=pi/6. Are you sure it's pi/3? (Not a rhetorical question; it wouldn't be the first time I made an algebra mistake!)
 
  • #5
Thanks for your help! I rellay didn't have any idea how to progress. That trigonometric identity really helps a lot.

Yes its a multiple choice question and the correct option according to the solution is [tex]\pi/3[/tex].
 

FAQ: Signal Analysis - Using Convolution

What is signal analysis using convolution?

Signal analysis using convolution is a mathematical method used to analyze signals in order to understand their properties and behavior. It involves convolving a signal with a filter or kernel in order to extract useful information from the signal.

How does convolution work?

In convolution, the filter or kernel is multiplied with each data point in the signal and then the resulting values are summed. This process is repeated for every data point in the signal, resulting in a new signal that contains information about the original signal and the filter. This process is also known as sliding window multiplication and summation.

What are the applications of signal analysis using convolution?

Convolution is commonly used in various fields such as image processing, audio processing, and signal processing. It can be used for tasks such as noise reduction, feature extraction, and pattern recognition.

What are the advantages of using convolution for signal analysis?

One of the main advantages of using convolution for signal analysis is that it can extract relevant features from a signal while reducing noise. It also allows for a more efficient and accurate analysis of signals compared to other methods.

Are there any limitations to signal analysis using convolution?

While convolution is a powerful tool for signal analysis, it does have some limitations. One limitation is that it assumes that the signal and the filter are stationary, meaning they do not change over time. Additionally, the accuracy of the analysis can be affected by the choice of filter and the size of the kernel used.

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