System Resonse's Autocorrelation Function (using integral)

In summary, the conversation discusses finding the output's autocorrelation function for a process that goes through a system with impulse response given by h(t) = u(t)e^{-bt}. The attempted solution involves separating the integral and using limits of integration, but the correct solution is found by realizing that the symbolic solution to the second integral can be used for the first half as well. This results in the same answer as using the power spectral density.
  • #1
RoshanBBQ
280
0

Homework Statement


x(t) is wss.
[tex] R_x(\tau) = e^{-a|\tau|}[/tex]
The process goes through a system with the impulse response
[tex] h(t) = u(t)e^{-bt}[/tex]
What is the output's autocorrelation function?

The Attempt at a Solution


For starters, I already found a solution by finding the output power spectral density and finding its inverse Fourier transform. But I cannot make my integration method work. I want someone to tell me where my integration is going wrong:

[tex]R_y(\tau)=\int_{-\infty}^\infty \int_{-\infty}^\infty h(s)h(r)R_x(\tau +s -r)\,dr \,ds [/tex]
[tex]R_y(\tau)=\int_{-\infty}^\infty \int_{-\infty}^\infty u(s)e^{-bs}u(r)e^{-br}e^{-a|\tau +s -r|}\,dr \,ds [/tex]

So I separated the integral. Let us just ignore the second integral for now and focus on my method for the first half. If the first half is working, the second will work. So I write for one integral:
[tex] \tau +s -r > 0 \rightarrow \tau + s > r[/tex]
So the first integral becomes, now eliminating the u(r) by incorporating it into the limits:
[tex]R_y(\tau)=\int_{-\infty}^\infty \int_{0}^{\tau + s} u(s)e^{-bs}e^{-br}e^{-a(\tau +s -r)}\,dr \,ds [/tex]

But we then realize unless the below condition is true, the integration adds up to zero due to the unit step:
[tex] \tau + s > 0 \rightarrow s > -\tau[/tex]

We can use this now as a limit of integration:

[tex]R_y(\tau)=\int_{-\tau}^\infty \int_{0}^{\tau + s} u(s)e^{-bs}e^{-br}e^{-a(\tau +s -r)}\,dr \,ds [/tex]

But, due to u(s), we know the following condition must also be true or the answer is zero:
[tex] -\tau > 0[/tex]
EDIT: I think the above is not true. If -tau < 0, the integration then goes from 0 to infinity. Is this the root of the error? If so, how do I handle this? uhh, I guess I'm just stuck.So I integragted (assuming this solution was for negative tau)
[tex]R_y(\tau)=\int_{-\tau}^\infty \int_{0}^{\tau + s} e^{-bs}e^{-br}e^{-a(\tau +s -r)}\,dr \,ds [/tex]

and got (for tau < 0)
[tex]\frac{e^{b \tau}}{2(a+b)b}[/tex]

So my total answer will be
[tex]\frac{e^{-b |\tau|}}{2(a+b)b} \ne \frac{1}{a^2-b^2} \left ( \frac{a}{b} e^{-b|\tau|} - e^{-a |\tau|} \right )[/tex]Note, the answer it is not equal to is the one I got using power spectral density.
 
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  • #2
OMG I GOT IT TO WORK USING THE REALIZATION I HAD IN MY EDIT!

So let's say I have the symbolic solution to that integral (after the second one) in terms of s. I will call it

x(s).

Let's say I set up a similar one for the other half and get that solution, calling it xx(r).

Then the solution for when tau < 0 is actually:
-x(tau) - xx(0)
(because x(inf) = 0 and xx(inf) = 0 -- I am just adding the two integrals, one from -tau to inf and the other from 0 to inf)

And this results in the same answer as from the power spectral density!
 

What is the System Response's Autocorrelation Function?

The System Response's Autocorrelation Function is a mathematical tool used to determine the correlation between a system's input and output signals. It is a measure of how much the output of a system is influenced by its past inputs.

How is the Autocorrelation Function calculated?

The Autocorrelation Function is calculated by taking the integral of the product of the system's input and output signals over a specific time interval. This integral is then divided by the square of the input signal's standard deviation, resulting in a normalized measure of correlation.

What does the Autocorrelation Function tell us about a system?

The Autocorrelation Function can provide insights into a system's behavior, such as whether it is linear or nonlinear, how quickly it responds to inputs, and if it has any internal feedback mechanisms. It can also help in identifying any noise or disturbances present in the system.

How is the Autocorrelation Function used in signal processing?

The Autocorrelation Function is commonly used in signal processing to analyze and characterize signals. It can help in detecting repeating patterns, identifying the frequency components of a signal, and estimating the system's impulse response. It is also used in correlation-based filtering techniques.

What are the limitations of using the Autocorrelation Function?

One limitation of the Autocorrelation Function is that it assumes the system is stationary, meaning that its behavior does not change over time. It also requires a large amount of data to accurately estimate the correlation between input and output signals. Additionally, it may not be suitable for non-linear systems or signals with high levels of noise.

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