Need help in assigning frequency and normalising amplitude to the fft plot

Question

Yogesh 2024-4-18

0
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此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2108626-need-help-in-assigning-frequency-and-normalising-amplitude-to-the-fft-plot

评论： Paul 2024-4-18

采纳的回答： Star Strider

在 MATLAB Online 中打开

clear all

close all

clc

L=10;

n=1.45;

c=2.9979e8;

dt = 6e-12;

T=10*2*L*n/c;

t = (-T/2/dt:1:T/2/dt)*dt;

Nt=round(T/dt);

fsine = 1e9;

vsine = 1;

phi = vsine*sin(2*pi*fsine*t);

FP=fft(phi);

fs=(-Nt/2:1:Nt/2-1);

Y=plot(fs,fftshift(abs(FP)));

Basically i have obtained this fft plot but I cannot figure out how to normalise the amplitude and then assign the frequency values.

Any help would be much appreciated.

Thank you.

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Answer 1

Star Strider 2024-4-18

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/2108626-need-help-in-assigning-frequency-and-normalising-amplitude-to-the-fft-plot#answer_1443806

在 MATLAB Online 中打开

There may be several approaches to your problem.

One of them is this —

clear all

close all

clc

L=10;

n=1.45;

c=2.9979e8;

dt = 6e-12;

T=10*2*L*n/c;

t = (-T/2/dt:1:T/2/dt)*dt;

Fs = 1/dt % Sampling Frequency

Fs = 1.6667e+11

Fn = Fs/2 % Nyquist Frequency

Fn = 8.3333e+10

Nt=round(T/dt);

fsine = 1e9;

vsine = 1;

phi = vsine*sin(2*pi*fsine*t);

FP=fft(phi)/numel(phi); % Normalise By Dividing By Signal Length

% fs=(-Nt/2:1:Nt/2-1)

fs = -Fn : Fs/numel(FP) : (Fn - Fs/numel(FP)); % Frequency Vector

figure

Y=plot(fs,fftshift(abs(FP)));

figure

Y=plot(fs,fftshift(abs(FP)));

grid

xlim([-1 1]*2.0E+9)

The signal energy is split between the +ve and -ve frequencies, so the frequency domain magnitude is only about ½ the original signal amplitude.

.

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Star Strider 2024-4-18

在 MATLAB Online 中打开

My pleasure!

To quote from my Answer —

‘The signal energy is split between the +ve and -ve frequencies, so the frequency domain magnitude is only about ½ the original signal amplitude.’

There is alkso ‘spectral leakage’, ao some of the energy also appears in the curved segments at the base of the peaks, as noited in the added third figure —

clear all

close all

clc

L=10;

n=1.45;

c=2.9979e8;

dt = 6e-12;

T=10*2*L*n/c;

t = (-T/2/dt:1:T/2/dt)*dt;

Fs = 1/dt % Sampling Frequency

Fs = 1.6667e+11

Fn = Fs/2 % Nyquist Frequency

Fn = 8.3333e+10

Nt=round(T/dt);

fsine = 1e9;

vsine = 1;

phi = vsine*sin(2*pi*fsine*t);

FP=fft(phi)/numel(phi); % Normalise By Dividing By Signal Length

% fs=(-Nt/2:1:Nt/2-1)

fs = -Fn : Fs/numel(FP) : Fn; % Frequency Vector

fs(ceil(numel(fs)/2)) = [];

Q = 1x2

161224 80612

figure

Y=plot(fs,fftshift(abs(FP)));

figure

Y=plot(fs,fftshift(abs(FP)));

grid

xlim([-1 1]*2.0E+9)

figure

Y=plot(fs,fftshift(abs(FP)));

grid

xlim([-1 1]*2.0E+9)

ylim([0 0.025])

text(0, 0.0015, '\leftarrow Spectral Leakage \rightarrow', 'Horiz','center')

Because of computational issues that are simply part of computer floating-point arithmetic, some of the signal energy also appears near the centres of the peaks. That is the reason the individual peak amplitudes do not equal exactly ½ of the original signal amplitude. The original signal energy being divided between the +ve and -ve frequency regions is the reason the peak magnitudes would only reach ½ the original signal amplitude if the computations were perfect (or if the signal was ‘windowed’ using for example an hann window, to correct for the fft being finite), while the analytic Fourier transform would be integrated from

to

and would therefore be exact.

.

Paul 2024-4-18

在 MATLAB Online 中打开

How does floating point accuracy affect this problem?

Are you suggesting that using a hann window would exactly recover the 1/2 amplitude? It doesn't seem to.

L=10;
n=1.45;
c=2.9979e8;
dt = 6e-12;
T=10*2*L*n/c;
t = (-T/2/dt:1:T/2/dt)*dt;
Fs = 1/dt;                                                       % Sampling Frequency
Fn = Fs/2;                                                       % Nyquist Frequency
Nt=round(T/dt);
fsine = 1e9;
vsine = 1;
phi = vsine*sin(2*pi*fsine*t);
FP=fft(phi(:).*hann(numel(phi)))/sum(hann(numel(phi)));                                         % Normalise  By Dividing By Signal Length
% fs=(-Nt/2:1:Nt/2-1)