Zero-mean Gaussian white-noise process with known power spectral density (PSD)
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Hello everybody
How can I generate zero-mean Gaussian white-noise process with known power spectral density (PSD)which is a constant ? (I want to add this noise to some acceleration data to model an accelerometer sensor)
Thanks in advance.
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Accepted Answer
Wayne King
on 14 May 2012
Just use randn() and multiply by the square root of the variance.
The theoretical PSD of WGN is \sigma^2 (\Delta t) where \sigma^2 is the variance and \Delta t is the sampling interval.
For example:
sigma2 = 2;
x = sqrt(sigma2)*randn(100,1);
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Noel
on 8 May 2013
I question this answer. pcov(2*randn(10000,1)) = 1 pcov(10*randn(10000,1)) = 15 pcov(100*randn(10000,1) = 35 How does this fit into "The theoretical PSD of WGN is \sigma^2 (\Delta t) where \sigma^2 is the variance and \Delta t is the sampling interval."
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