Power of noise after passing through a system h(t)

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SUMMARY

This discussion focuses on calculating the variance of noise after passing through a Linear Time-Invariant (LTI) system characterized by its impulse response h(t). The user seeks to demonstrate that for white noise modeled by Rxx(τ) = σx² δ(τ), the variance σy² can be expressed as σy² = σx² ∫h²(u) du. Conversely, for 1/f noise modeled by Rxx(τ) = σx², the variance is σy² = σx²(∫h(u) du)². The user also inquires about the validity of rewriting the variance equation under the assumption of a stationary process.

PREREQUISITES
  • Understanding of Linear Time-Invariant (LTI) systems
  • Knowledge of autocorrelation functions in signal processing
  • Familiarity with white noise and 1/f noise concepts
  • Basic calculus, particularly integration techniques
NEXT STEPS
  • Study the properties of Linear Time-Invariant (LTI) systems in signal processing
  • Learn about autocorrelation functions and their applications in noise analysis
  • Research the mathematical treatment of white noise and 1/f noise
  • Explore advanced integration techniques relevant to signal processing
USEFUL FOR

Students in electrical engineering, signal processing professionals, and anyone interested in the statistical analysis of noise in LTI systems.

iVenky
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**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

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
 
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Hi,
Can you tell me if there is a better forum for this question to get an answer?
 

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