Signal to Noise Ratio: Definition & Wiener Filter Calculation

AI Thread Summary
Signal to noise ratio (SNR) is defined as the measure of signal power relative to noise power, and calculating it with a Wiener filter involves analyzing the input signal and noise characteristics. The Wiener filter is designed to enhance the SNR by processing the signal, which typically has a narrow spectrum, against noise that has a broader spectrum. To determine the SNR improvement, one must apply the filter to both the signal and noise and evaluate the power levels at the output. The discussion emphasizes the importance of understanding Wiener design principles and how to compute the SNR of the output after filtering. Ultimately, effective use of the Wiener filter can lead to significant enhancements in signal clarity.
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What is signal to noise ratio ? And how do I calculate it with the wiener filter?
 
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You need to know how the signal and the noise look like at the filter input and calculate what they will be at the output.
Usually a signal has a narrow spectrum, while the noise has a broad (almost flat) spectrum.
Apply a filter to that and see how much power is in signal and how much is in noise at the output of the filter.
 
What are you using the filter to do? Since a wiener filter can be used to improve signal to noise, are you actually trying to calculate the signal to noise ratio improvement by your filter?

Do you understand wiener design? Do you understand this? http://en.wikipedia.org/wiki/Wiener_filter
 
I am using it to process a signal say x(n) = sin(nw+theta) +w(n) where w(n) is random noise and say I use a 2 and 3 tap and get wiener values …. [.5 ; -.4] and [-6 ; 15; -5] (note: these are not actual values). I want to calculate the SNR of the interference and SNR of the output. Is the SNR of the output just processing x(n) through each filter i.e. 1/(1-.5z^-1) and then 1/(z-.4z^-1)?
 
Yes, apply the filter on the signal (sin(...)) and the noise (w) and see what happens.
 
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