Remove specific frequencies from FFT signal and reconstruct the signal after filtering those frequencies
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Hi,
I have a signal that shows a very distinctive peaks in the FFT.
Those high amplitudes are the 'noise' of the signal. I would like to remove that values from the original signal and to plot the filtered signal.
Fs = 4500; % Sampling frequency (fps)
T = 1/Fs; % Sampling period (s)
L = 900; % Length of signal (how many frames)
tt = (0:L-1)*T; % Time vector
thickness = detrend(thickness);
Y = fft(thickness);
P2 = abs(Y/L);
P1 = P2(1:L/2+1);
P1(2:end-1) = 2*P1(2:end-1);
f = Fs*(0:(L/2))/L;
figure(1111)
h1=plot(f(1:end),P1(1:end)) ;
title('Amplitude Spectrum')
xlabel('f [Hz]')
ylabel('Power [mm]')
ylim auto
[B,IX] = sort(P1); %order the amplitudes
A1=B(end); %amplitude of the first peak
A2=B(end-1); %amplitude of second peak
f1=f(IX(end)); %frequency of first peak
f2=f(IX(end-1)); %frequency of second peak
AmpTab=[A1 A2];
FreTab=[f1 f2];
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Bjorn Gustavsson
2020-11-12
Those high amplitudes are a noise in the signal. Not the noise in the signal. To remove such interference-components you will be better off doing it this way (remember the Fourier-transform of a real signal has symmetric real components and an anti-symmetric imaginary components, and also that the DC-component is real.):
fD = fft(data_t_tichkness(:,2)); % Discrete Fourier-transform of your data
subplot(2,1,1)
plot(abs(fD)) % Plot of its absolute values
hold on
[safD,idx] = sort(abs(fD),'descend'); % Sort in descending order, this makes indexing simpler
plot(idx(2:5),abs(fD(idx(2:5))),'r.') % DC-component will be the first, then
% the positive and negative components will
% have equal magnitudes and appear consecutively in idx
fD(idx(2:5)) = 0; % Set the Fourier-components of the first and second spike to zero.
plot(abs(fD)) % Yup, they're gone.
subplot(2,1,2)
ifD = ifft(fD); % inverse-Fourier-transform
plot(data_t_tichkness(:,2))
hold on
plot(ifD)
HTH
7 个评论
Bjorn Gustavsson
2020-12-7
编辑:Bjorn Gustavsson
2020-12-7
Well the lowpass filter should do what you want. If you're unsure about the filter characteristics you can reasonably easy simply check the documentation (it contains a lot of juicy information!), test what frequency response you get from a single delta-spike as input etc.
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