MATLAB: The use of IFFT

Can any kind soul please help or provide some kickstart?

I have a transfer function/frequency response, h(f). Where h(f) is calculated by applying FFT to the csv data and then V2(f) is divided with V1(f).

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Your data are frequency and only the corresponding amplitudes of the input and output signal at each frequency. You need to measure the phase data at each frequency as well, and since this is missing, half of your data are missing.

You could calculate the transfer function from the complex fft of both the input and output time domain data, but lacking the phase data, reconstructing the impulse response is not possible.

If you have complex frequency data, you can use the Signal Processing Toolbox invfreqz function to estimate the transfer function, and then use impz to calculate the impulse response.

Related Solutions

MATLAB: Single sided spectrum of vector y obtained using fft

The way I usually calculate and plot it:

t = linspace(0, 1, 1E+4);
s = sum(sin([0; 100; 200; 300]*2*pi*t*10)) .* randn*10;
Ts = mean(diff(t));                                         % Sampling Interval
Fs = 1/Ts;                                                  % Sampling Frequency
Fn = Fs/2;                                                  % Nyquist Frequency
L1 = numel(t);
FTs1 = fft(s)/L1;                                           % FFT Of Original Signal
Fv1 = linspace(0, 1, fix(L1/2)+1)*Fn;                       % Frequency Vector

Iv1 = 1:numel(Fv1);                                         % Index Vector

L2 = 2^nextpow2(L1);
FTs2 = fft(s,L2)/L1;                                        % FFT Of Zero-Padded Signal
Fv2 = linspace(0, 1, fix(L2/2)+1)*Fn;                       % Frequency Vector
Iv2 = 1:numel(Fv2);                                         % Index Vector
figure
subplot(2,1,1)
plot(Fv1, abs(FTs1(Iv1))*2)
title('FFT of Original Signal')
xlabel('Frequency (Hz)')
ylabel('Amplitude')
grid
subplot(2,1,2)
plot(Fv2, abs(FTs2(Iv2))*2)
title('FFT of Zero-Padded Signal')
xlabel('Frequency (Hz)')
ylabel('Amplitude')
grid

This also demonstrates a way to increase the frequency resolution of the fft result by zero-padding it.

MATLAB: Looking for low frequncy peaks from the FFT of a time series signal

Your frequency peaks are much smaller than the d-c offset in ‘dsignal’, so the d-c offset will dominate the Fourier spectrum. The easiest way to eliminate it is to subtract the d-c offset (mean value of your signal) from the signal before taking the Fourier transform of it.

Try this:

L = numel(dsignal);
Fs = 25;                                                % Sampling Frequency (Hz)
Fn = Fs/2;                                              % Nyquist Frequency (Hz)
t = linspace(0, 1, L)/Fs;                               % Time Vector
y = fft(dsignal - mean(dsignal))/L;                     % Fourier Transform, Normalise For Length
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                     % Frequency Vector (One-Sided Fourier Transform)
Iv = 1:numel(Fv);                                       % In \dex Vector
figure
plot(Fv, abs(y(Iv))*2)
grid
xlim([0 0.5])

That will clearly show the 3 low-frequency peaks, and some of the low-frequency noise in the rest of the signal.

Best Answer

Related Solutions

MATLAB: Single sided spectrum of vector y obtained using fft

MATLAB: Looking for low frequncy peaks from the FFT of a time series signal

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