I have a set of samples values in time domain. I know they are uncorrelated and I have to extract the correct amplitudes. However, the values are only ~88% of what they should be.
As a test see the minimal working example in R below. I think it can be understood even if you are not familiar with the language. I generate the data uncorrelated data with rnorm() and then apply fft(). As a consistency check I show that Parseval's theorem is fulfilled, which it is. Is there anything I am not aware of?
MWE:
# dt <- 0.01 # time stesp
nSteps <- 100000 # Number of time steps
# df <- 1/(nSteps*dt) # frequency resolution
# t <- 0:(nSteps-1)*dt #
y <- rnorm(nSteps, mean=0, sd=1) # generate uncorrelated data. Should result in a white noise spectrum with sd=1
y_sq_sum <- sum(y^2)
# We ignore cutting to the Nyquist frequency.
# f <- 0:(nSteps-1)*df
fft_y <- abs(fft(y))/sqrt(length(y))
fft_y_sq_sum <- sum(fft_y^2)
print(paste("Check for Parseval's theorem: y_sq_sum = ", y_sq_sum, "; fft_y_sq_sum = ", fft_y_sq_sum, sep=""))
print(paste("Mean amplitude of my fft spectrum: ", mean(fft_y)))
print(paste("The above is typically around 0.88, why is it not 1?"))
```
fft_y <- abs(fft(y))/sqrt(length(y))-- Allfftimplementations perform some form of normalization -- they usually divide by 1, by $N$, or by $\sqrt{N}$. Have you verified what R'sfftdoes? $\endgroup$ – MBaz Oct 10 '20 at 14:37