How can I calculate the SNR of a curve that has impulse noise added?

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Christopher Dyer
Christopher Dyer 2024-3-20
回答： Hornett 2024-9-4
Hi all,
I have an issue where I'm not sure how to calulate the SNR of a signal that I have in MATLAB. I have a real-world signal dataset that is a curve shape (a bit like a logarithmic curve) that I import into MATLAB from a .CSV. This is my reference curve. I then add impulse noise to the curve using the alpha stable distribution. This noisy signal is then put through a rolling mean filter. I also smooth a separate instance of the noisy curve using the arithmetic mean. I want to compare the SNR of the curve signal and demonstrate how the filter has improved the noisy signal. How can I do this?
Importing the signals x100
cd 'C:blah\blah\'
z_numfiles = 100;
for k = 1:z_numfiles
myfilename = sprintf('scope_%d.csv', k);
nometal{k} = xlsread(myfilename, 'C4:C500');
end
This is two instances of the "raw" signal capture
Adding the impulse noise to the "nometal" signal and increasing the size of the dataset from x100 to x1000:
pd1 = makedist('Stable','alpha',0.5,'beta',0,'gam',0.001,'delta',0);
temp = 0;
distr_puls_nometal = cell(1000, 1);
samplesize = 497;
loop_count = 1000;
for n = 1:9
for j = 1:loop_count
temp = cell2mat(nometal(n));
for i = 1:samplesize
a = random(pd1);
temp(i) = temp(i) + a;
if temp(i) > 3
temp(i) = 3;
elseif temp(i) < 0
temp(i) = 0;
end
end
distr_puls_nometal{n+j} = temp;
end
end
Comparing a rolling mean and an arithmetic mean of the noisy "no metal" signal:
How can I calculate the SNR decrease for the "noisy" signal? How can I caculate the signal improvement by attenuating the noise? I also have a number of other noise distributions but I assume that the process will be the same. Thanks all!
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回答（1 个）

Hornett
Hornett 2024-9-4
To calculate the SNR (Signal-to-Noise Ratio) of a signal in MATLAB, you can follow these steps:
% Calculate the power of the signal by squaring each sample and taking the mean:
signal_power = mean(signal.^2);
% Calculate the power of the noise by subtracting the signal from the noisy signal and taking the mean:
noise_power = mean((noisy_signal - signal).^2);
% Calculate the SNR by dividing the signal power by the noise power:
snr = 10 * log10(signal_power / noise_power);
To demonstrate the improvement in the noisy signal after applying the rolling mean filter, you can calculate the SNR before and after filtering and compare the results.
Hope this helps!

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