Calculation of the error function.

Click For Summary
The discussion revolves around calculating the error function ε = E((X(t)-Y(t))^2) for the random process Y(t) = G(t)X(t), where G(t) is a cosine function with a uniformly distributed phase ψ. The user is attempting to transition to the frequency domain using the low pass filter H(Ω) and is unsure how to apply the law of total expectation in this context. They express confusion about the correct conditioning and notation, particularly while denoting Z(Ω) = Y(Ω)H(Ω). The user ultimately seeks clarification on how to compute the expected value of the squared difference between X(t) and Z(t).
MathematicalPhysicist
Science Advisor
Gold Member
Messages
4,662
Reaction score
372
I have the next two signals:

X(t) and G(t) and a random process Y(t)=G(t)X(t) where X(t) and G(t) are wide sense stationary with expectation values: E(X)=0, E(G)=1.

Now, it's also given that ##G(t)=\cos(3t+\psi)## where ##\psi## is uniformly distributed on the interval ##(0,2\pi]## and is statistically independent of X(t).

The signal X(t) is transferred through a low pass filter, given in the frequency domain as ##H(\Omega)=1## when ##\Omega \leq 4\pi## and otherwise zero.

I am given that ##Y(\Omega)=X(\Omega)H(\Omega)##, and I want to calculate:

##\epsilon = E((X(t)-Y(t))^2)##

I guess I can go to the frequency domain, but I also need to use the http://en.wikipedia.org/wiki/Law_of_total_expectation

But I am not sure how exactly to condition this, thanks in advance.
 
Last edited by a moderator:
Physics news on Phys.org
For LaTeX, use either $$ on each end or ## (for inline LaTeX) on each end of your expressions. The $ pair is pretty much equivalent to [ tex ] and the # pair is equivalent to [ itex ] (all without extra spaces inside the brackets).
 
Hmmmmm... I am not that expert but isn't it

epsilon = E((X(t)-Y(t))^2) = E((X-G X)^2)= E((X(1-G))^2) = E(X^2 (1-G)^2) =
E (X^2)*E((1-G)^2) = E^2(X)*E^2(1-G) = 0
 
Sorry, I have abuse of notation, I should have denoted:
$$Z(\Omega)=Y(\Omega)H(\Omega)$$

And I am looking for $$E((X(t)-Z(t))^2)$$

I was tired yesterday evening, a long exam that day.
 
First trick I learned this one a long time ago and have used it to entertain and amuse young kids. Ask your friend to write down a three-digit number without showing it to you. Then ask him or her to rearrange the digits to form a new three-digit number. After that, write whichever is the larger number above the other number, and then subtract the smaller from the larger, making sure that you don't see any of the numbers. Then ask the young "victim" to tell you any two of the digits of the...

Similar threads

  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 2 ·
Replies
2
Views
2K
  • · Replies 3 ·
Replies
3
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 0 ·
Replies
0
Views
2K
Replies
3
Views
2K
  • · Replies 11 ·
Replies
11
Views
2K
Replies
8
Views
2K
  • · Replies 17 ·
Replies
17
Views
1K