Specter of a square signal(10Khz) at 10MHz frequency

2 views (last 30 days)
I want to draw a frequency spectre of a square signal. Sampling is being performed at 10MHz, i want to see 4 polygons of that frequency
My current code is like this
close all;
%Define number of samples to take
fs = 100e3; %%% 100 kHz
f0 = 10e3; %Hz
%Define signal
t = 0:1/fs:1-1/fs;
dT = length(y);
t1=(0:dT-1)/f0/100;
dolzina=length(t);
y= zeros(1,dolzina);
y(1:fix(dolzina/2))=1;
y=[y y y y y y y y y y y y y y y y y y y y y y y y y y y y y y];
signal = y;
figure(1)
plot(t1, signal);
%Take fourier transform
fftsignal = fft(y);
%apply fftshift to put it in the form
fftsignal = fftshift(fftsignal);
%Next, calculate the frequency axis, which is defined by the sampling rate
f = fs/2*linspace(0,30,fs);
fq = length(f)
%Since the signal is complex, we need to plot the magnitude to get it to
%look right, so we use abs (absolute value)
figure(2)
plot(f, abs(fftsignal));
i get an error, that i can't plot the transformed signal because of vectors not being the same length
can anyone help?

Accepted Answer

Mathieu NOE
Mathieu NOE on 14 Jan 2021
hello
there is a much faster method to generate a square signal (using square )
see demo below for signal generation and fft analysis
NB your sampling freq is 100 kHz in the code, not 10 MHz as stated in the post
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% load signal
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% dummy data
Fs = 100e3; % sampling freq
f0 = 10e3; % signal freq
duration = 1; % signal duration (s)
samples = Fs*duration;
t = (0:samples-1)*1/Fs;
% signal = square(2*pi*f0*t); % neg / pos symetrical square wave ( -1 / +1)
signal = 0.5*(square(2*pi*f0*t)+1); % 0 / pos square wave ( 0 / +1)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% FFT parameters
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
NFFT = 10000; %
Overlap = 0.75;
w = hanning(NFFT); % Hanning window / Use the HANN function to get a Hanning window which has the first and last zero-weighted samples.
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% display : averaged FFT spectrum
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
[freq,fft_spectrum] = myfft_peak(signal, Fs, NFFT, Overlap);
sensor_spectrum_dB = 20*log10(fft_spectrum);% convert to dB scale (ref = 1)
figure(1),plot(freq,sensor_spectrum_dB,'b');grid
title(['Averaged FFT Spectrum / Fs = ' num2str(Fs) ' Hz / Delta f = ' num2str(freq(2)-freq(1)) ' Hz ']);
xlabel('Frequency (Hz)');ylabel(' dB')
text(locs+.02,pks,num2str(freq(locs)))
function [freq_vector,fft_spectrum] = myfft_peak(signal, Fs, nfft, Overlap)
% FFT peak spectrum of signal (example sinus amplitude 1 = 0 dB after fft).
% Linear averaging
% signal - input signal,
% Fs - Sampling frequency (Hz).
% nfft - FFT window size
% Overlap - buffer overlap % (between 0 and 0.95)
signal = signal(:);
samples = length(signal);
% fill signal with zeros if its length is lower than nfft
if samples<nfft
s_tmp = zeros(nfft,1);
s_tmp((1:samples)) = signal;
signal = s_tmp;
samples = nfft;
end
% window : hanning
window = hanning(nfft);
window = window(:);
% compute fft with overlap
offset = fix((1-Overlap)*nfft);
spectnum = 1+ fix((samples-nfft)/offset); % Number of windows
% % for info is equivalent to :
% noverlap = Overlap*nfft;
% spectnum = fix((samples-noverlap)/(nfft-noverlap)); % Number of windows
% main loop
fft_spectrum = 0;
for i=1:spectnum
start = (i-1)*offset;
sw = signal((1+start):(start+nfft)).*window;
fft_spectrum = fft_spectrum + (abs(fft(sw))*4/nfft); % X=fft(x.*hanning(N))*4/N; % hanning only
end
fft_spectrum = fft_spectrum/spectnum; % to do linear averaging scaling
% one sidded fft spectrum % Select first half
if rem(nfft,2) % nfft odd
select = (1:(nfft+1)/2)';
else
select = (1:nfft/2+1)';
end
fft_spectrum = fft_spectrum(select);
freq_vector = (select - 1)*Fs/nfft;
end
  4 Comments
Mathieu NOE
Mathieu NOE on 14 Jan 2021
hello again
regarding your comment : the signal have lower amplitude of 0 and higher amplitude of 1
is exactly what I suspected , so this is the simplest way to generate it (as in my code) :
signal = 0.5*(square(2*pi*f0*t)+1); % 0 / pos square wave ( 0 / +1)

Sign in to comment.

More Answers (0)

Categories

Find more on Fourier Analysis and Filtering in Help Center and File Exchange

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!