- #1
iVenky
- 212
- 12
**Reposting this again, as I was asked to post this on a homework forum**
1. Homework Statement
Hi,
I am trying to solve this math equation (that I found on a paper) on finding the variance of a noise after passing through an LTI system whose impulse response is h(t)
X(t) is the input noise of the system and Y(t) is the output noise after system h(t)
if let's say variance of noise Y(t) is
σy2=∫∫Rxx(u,v)h(u)h(v)dudv
where integration limits are from -∞ to +∞. Rxx is the autocorrelation function of noise X. Can you show that if Rxx (τ)=σx2 δ(τ) (models a white noise), then
σy2=σx2∫h2(u)du (integration limits are from -∞ to +∞)
and if Rxx (τ)=σx2 (models a 1/f noise), then
σy2=σx2(∫h(u)du)2 (integration limits are from -∞ to +∞)
I don't understand the math behind statistics that well
Thanks
Can I write σy2=∫∫Rxx(u,v)h(u)h(v)dudv as
σy2=∫∫Rxx(τ)h(u)h(u+τ)dudτ
if noise X(t) is stationary process?
However, I am not sure how Rxx (τ)=σx2 δ(τ) or σx2 results in those different equations shown above
Same as before
1. Homework Statement
Hi,
I am trying to solve this math equation (that I found on a paper) on finding the variance of a noise after passing through an LTI system whose impulse response is h(t)
X(t) is the input noise of the system and Y(t) is the output noise after system h(t)
if let's say variance of noise Y(t) is
σy2=∫∫Rxx(u,v)h(u)h(v)dudv
where integration limits are from -∞ to +∞. Rxx is the autocorrelation function of noise X. Can you show that if Rxx (τ)=σx2 δ(τ) (models a white noise), then
σy2=σx2∫h2(u)du (integration limits are from -∞ to +∞)
and if Rxx (τ)=σx2 (models a 1/f noise), then
σy2=σx2(∫h(u)du)2 (integration limits are from -∞ to +∞)
I don't understand the math behind statistics that well
Thanks
Homework Equations
Can I write σy2=∫∫Rxx(u,v)h(u)h(v)dudv as
σy2=∫∫Rxx(τ)h(u)h(u+τ)dudτ
if noise X(t) is stationary process?
However, I am not sure how Rxx (τ)=σx2 δ(τ) or σx2 results in those different equations shown above
The Attempt at a Solution
Same as before