Help with covariance calculation please

In summary, the author is trying to understand a function given by: x(t) = \sum_i A_i s(t-iT-\tau_i)+n(t) where: - \left\{A_i\right\}_{i\in Z} is a random point process, which is assumed to be periodically correlated with a period Q longer than T - \left\{\tau_i\right\}_{i\in Z} is a zero-mean delta-correlated point process with probability density function \phi_{\tau}(\tau_i) ,- \left\{n
  • #1
crimolvic
3
0
Hello,

I have trying to understand this excercise for quite a time, but still with no results. So I thought maybe you can help me ;P

Here is the problem. I have a function given by:
[tex] x(t)=\sum_i A_i s(t-iT-\tau_i)+n(t) [/tex]
where:
- [tex] \left\{A_i\right\}_{i\in Z} [/tex] is a random point process, which is assumed to be periodically correlated with a period [tex] Q [/tex] longer than [tex] T [/tex]
- [tex] \left\{\tau_i\right\}_{i\in Z} [/tex] is a zero-mean delta-correlated point process with probability density function [tex] \phi_{\tau}(\tau_i) [/tex],
- [tex] \left\{n(t)\right\}_{t\in R} [/tex] is a zero-mean stationary process

The covariance function os a function[tex] x(t) [/tex] defined as follows:
[tex] C_{xx}(t,\tau)=E\left\{[x(t+\tau/2)-m_x(t+\tau/2)][x(t-\tau/2)-m_x(t-\tau/2)]\right\} [/tex]
where [tex] m_x(t) [/tex] is the mathematical expectation (ensemble average) of [tex] x(t) [/tex]. For our function one obtains [tex] m_x(t)=\sum_i\bar{A}_i s(t-iT)\ast\phi_{\tau}(t-iT) [/tex]. So after substitution of [tex] m_x(t) [/tex] on the expression defining [tex] C_{xx}(t,\tau) [/tex] one could obtain the covariance function. By assuming that the processes [tex] \left\{A_i\right\}_{i\in Z} [/tex], [tex] \left\{\tau_i\right\}_{i\in Z} [/tex] and [tex] \left\{n(t)\right\}_{t\in R} [/tex] are mutually uncorrelated, one obtains somehow (and here is where I need your help, please) the result:

[tex] C_{xx}(t,\tau)=\sum_i \bar{A_i^2}[s(t+\tau/2-iT)s^{\ast}(t-\tau/2-iT)]\ast \phi_{\tau}(t)-\sum_i \bar{A}_i^2 \widetilde{s}(t+\tau/2-iT)\widetilde{s}^{\ast}(t-\tau/2-iT) + C_{nn}(\tau) [/tex]

where [tex] \widetilde{s}(t)=s(t)\ast \phi_{\tau}(t) [/tex] (convolution) and the upperscript [tex]\ast[/tex] on for example [tex]s^{\ast} [/tex] stands for the complex conjugate.

I took the problem from the article "the relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals" from Randall, et. al published on Mechanical Systems and Signal Processing (2001) 15(5), 945-962, under the point 2.

thanking in advance,
crimolvic
 
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  • #2
Ups, it seems I did something wrong on using the Latex cappability of the forum. Does someone know how can I fix this?

crimolvic
 
  • #3
Don't put spaces inside the brackets: [tex] e^{x^2}[/tex]
not [ tex ]e^{x^2}[ /tex ]. Click on the LaTex above to see the difference.
 
  • #4
thanks HallsofIvy, now is looking much better.
I hope now someone can help understand this problem
 
  • #5
It's going to have to be someone better at probability than I am!
 

FAQ: Help with covariance calculation please

What is covariance and why is it important?

Covariance is a measure of how two variables change together. It is important because it helps us understand the relationship between two variables and how they affect each other.

How do you calculate covariance?

Covariance is calculated by taking the sum of the product of the differences between each variable and their respective means, and then dividing by the total number of observations.

Can covariance be negative?

Yes, covariance can be negative. A negative covariance indicates that as one variable increases, the other variable decreases.

What is the difference between covariance and correlation?

Covariance measures the direction and strength of the relationship between two variables, while correlation measures the strength of the relationship between two variables on a scale of -1 to 1.

How is covariance used in data analysis?

Covariance is used in data analysis to understand the relationship between two variables and to determine if there is a pattern or trend in the data. It is also used to calculate other statistical measures, such as correlation and regression.

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