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Fast Fourier Transform Issue

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In the Matlab description of the FFT implementation ( There is this part where the vector Y is divided by the length L in order to compute the spectrum. I cannot understand why it is required to divide by L, if someone can maybe enlighten this point.
Compute the two-sided spectrum P2. Then compute the single-sided spectrum P1 based on P2 and the even-valued signal length L.
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
Thank you.
  1 Comment
dpb on 8 Jul 2021
In short, it's simply to normalize the PSD peak to the amplitude of the input time series -- if don't then the magnitude is proportional to the length of the input.
Just had long discussion (amongst many over the years) a short time ago. The final comment I added at illustrates what happens if one doesn't normalize;

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Accepted Answer

David Goodmanson
David Goodmanson on 9 Jul 2021
Hi Anas,
suppose you have N = 1000 points over an interval of one second. The time array is t = (0:999)/1000. [see note below]. Let f0 = 1 Hz and consider the complex waves
g_n(t) = exp(2*pi*i*n*f0*t) for n = 1,2,3, ....
Each g_n(t) oscillates n times in the time window.
The fft takes the signal, and for each m = 1,2,3 ... [1] multiplies the signal by g_m(t)* = exp(-2*pi*i*m*f0*t), [2] does a sum over all the array points and [3] reports out the answer.
Suppose your signal is a single oscillatory wave of amplitude 1 for a particular n0. After step [1] the fft will do a sum over all the points of the expression
B = exp(2*pi*i*(n0-m)*f0*t).
The result is 0 except when m = n0. In that case B = contant = 1 and the sum over the N points gives the result N. So if you want to recover the original amplitude 1 you have to divide the fft result by N.
[note] The array has 1000 points and 1000 intervals, including the interval from .999 sec to 1 sec, but does not include the point at 1 sec.
David Goodmanson
David Goodmanson on 10 Jul 2021
Every signal can be represented as the sum of complex waves with amplitude coefficients cn:
sig = Sum{n} cn*exp(2*pi*i*n*f0*t)
The fft determines the cn coefficients, which is the frequency spectrum. To do that multiply the signal by g_m(t)* = exp(-2*pi*i*m*f0*t), take the sum over points and divide by N, which gives, on the right hand side,
Sum{n} cn*exp(2*pi*i*(n-m)f0*t) /N
AsI mentioned before, the sum is N when n = m and zero otherwise. You get N*cm/N = cm. Do that for each m. That's the principle. As for the underlying software, that's proprietory with Matlab, but if you look up the Cooley-Tukey algorithm you will get the basic idea.

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