hello
I modifed a bit your code and tested it with a two tone signal
see the line with peak_fr_sorted_values
clc
%% dummy data
Fs = 1e3;
samples = 1e4;
t = (0:samples-1)'*1/Fs;
signal = 0.7*cos(2*pi*50*t)+0.4*cos(2*pi*100*t)+0.1*randn(samples,1);
[data_filt_ft, fr] = fr_t(signal, Fs);
%% Peak detection Part
% In this part, we have defined a threshold value. So, all peaks above
% threshold value will be detected.
temp_fft = data_filt_ft;
% Normalize the FFT
temp_fft = temp_fft/max(temp_fft);
% threshold
th = 0.02; % i.e. 1% of maximum amplitude
temp_fft(temp_fft < th) = 0; %you can skip this step by commenting this.
% Plot FFT after thresholding
figure; plot(fr, temp_fft);
xlabel('Frequency(Hz)'); ylabel('Amplitude');
grid on;
% find peaks of the remaining data
[peak_ft, peak_loc] = findpeaks(temp_fft);
%Sort amplitude values into ascending order
[peak_ft_sorted_values,peak_ft_sorted_index] = sort(peak_ft,'ascend');
% frequency corresponding to peak
[peak_fr] = fr(peak_loc);
% and now sorted according to amplitude (above)
peak_fr_sorted_values = peak_fr(peak_ft_sorted_index)
% this fuction returns the fourier transform of the signal
function [ft, f] = fr_t(x, Fs)
L = length(x);
% NFFT = 2^nextpow2(L); % Next power of 2 from length of y
NFFT = L;
X = fft(x,NFFT)/NFFT;
f = Fs/2*linspace(0,1,floor(NFFT/2+1));
ft = abs(X(1:floor(NFFT/2+1)));
% ft = 20*log10(ft);
% plot(f,ft);
end
