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I have a signal which is comprised of 4 chirp signals and an additive noise with the same sampling frequency and size is generated now i need to calculate the SNR of the signal and noise . Also if I am correct to vary the signal to noise ratio is it ok if I vary the amplitudes of chirp signals and also the noise by multiplying it with a factor : ex:

noise = randn(size(t));

where t = 0:1e-4:1;

and to increase the noise

{new noise = 2*noise ;}

is this correct?? and to increase the amplitudes of the signal is this the way to change the signal to noise ratio:

y3 = 5* chirp(t,600,t1,800,'linear');

y4 = 3.5*chirp(t,900,t1,980,'linear');

Geoff
on 7 Jul 2021

Edited: MathWorks Support Team
on 7 Jul 2021

From Wikipedia: http://en.wikipedia.org/wiki/Signal-to-noise_ratio

NR = Psignal / Pnoise = (Asignal / Anoise)^2

Where P is power, and A is amplitude. I would calculate the RMS amplitudes and use those in the above formula.

RMS means Root-Mean-Square. That is, you square your signal, calculate the mean of that, and take the square root. Just define a wee anonymous function for clarity:

RMS = @(x) sqrt(mean(x.^2));

Now you can compute your ratio like so:

RMS = @(x) sqrt(mean(x.^2));

Rohan Patni
on 3 Oct 2020

Because power of a signal is directly proportonal to the square of the said RMS amplitude

Wayne King
on 16 Apr 2012

You can increase the SNR by increasing the amplitude of the signal and by decreasing the variance of the noise. You have to remember that if you want to increase the variance of the noise by 2, you should multiply randn() by sqrt(2), not 2. Multiplying by two increases the variance by a factor of 4.

x = randn(100,1); % variance is 1

x = sqrt(2)*randn(100,1) % variance is 2

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