I want to create a sinusoidal wave that has the following properties :
a sine wave with f=400Hz amp=1 from 0 to 2s
a sine wave with f=200Hz amp=1 from 2 to 3s
a sine wave with f=800Hz amp=2 from 3 to 5s
Here is my matlab Code :
t=linspace(0,5,5000);
x=zeros(1,length(t));
n1=0:1999;
n2=2000:2999;
n3=3000:4999;
x(1:2000)=1*sin(2*pi*400*n1);
x(2001:3000)=1*sin(2*pi*200*n2);
x(3001:5000)=2*sin(2*pi*800*n3);
plot(t,x)
and here is the plot that I had, still it looks not logical at all,
So I would like to know the error in my code
In this type of problem, where you're naturally looking at physical quantities, it's very helpful to be consistent with this all the way through your calculations.
Specifically, you specify Hz (1/seconds), a physical unit, so when you calculate everything else, you need to be consistent with that.
To do this in your equation, it's most straightforward to put time directly in the sin function, like sin(2*pi*f*t). But since you want to break the array apart using different n, it probably easiest to do that and then use t=linspace(0,5,50000) and dt = 5.0/50000 or dt = t(2) - t(1), and sin(2*pi*400*dt*n1). Read this as dt*n1 converts the integers in n1 to time in seconds.
Note the physical units too: 400 in above is actually 400Hz, and the time is in seconds, so the units of 2*pi*400*dt*n1 and 2*pi*f*t are Hz * s = 1, that is, the units cancel, which is what you need.
There is a tendency for programmers to want to define away some unit, like say seconds=1. This is possible and technically correct and can save a multiplication or two. It almost always leads to errors.
Note also that you should change from t=linspace(0,5,5000) to something like t=linspace(0,5,50000). The reason should now be clear: you're looking at frequencies from 400-800Hz, or almost 1kHz, or 1 oscillation per millisecond. To see a sine wave, you'll need to get in a few data points per oscillation, and 50000 points in 5 seconds will now give about 10 points per millisecond, which is barely enough to see a reasonable sine wave. Or, however you want to think of the calculation, somehow you need to be sure you sample at a high enough rate.
That is, the specific error that your encountering is that by using integers instead of fractions of a second for your time array, you're taking much too large of steps for the sin function. That's always a possible problems with the sin function, but even if you did plot a sin that looked like a sin (say, by using a frequency like 0.003Hz instead of 400Hz) it would still be incorrect because it wouldn't have the proper time axis. So you need to both get the units correct, and make sure that you get enough data per oscillation to see the sine wave (or whatever it is you happen to be looking for).
Related
I have a model of the form:
All I am doing here is mixing the two sine waveforms based on the random bits.
I set the signal frequency of first sign wave to:
Second sign wave:
The output is :
But it works well when the signals are of low frequency.
How can I make it to work even at high frequencies ?
80*10^3*t and 12*10^4*t are both always an integer for all values of t=0:0.01:100. Hence the sin is always being evaluated at an integer multiple of 2*pi. Hence the value of the plot is always zero (or near enough to it down in the 10^-8 or 10^-9 range.
You need to change the sample rate so that you get points where sin is not zero.
From my understanding, when using the cpsd function as such:
[Pxy,f] = cpsd(x,y,window,Ns,NFFT,Fs);
matlab chops the time series data into smaller windows with size specified by you. And the windows are shifted by Ns data point. The final [Pxy, f] are an average of results obtained from each individual window. Please correct me if I am wrong about this process.
My question is, if I use angle(Pxy) at a specific frequency, say 34Hz. Does that give me the phase difference between signal x and y at the frequency 34Hz?
I am having doubt about this because if Pxy was an average between each individual window, and because each individual was offset by a window shift, doesn't that mean the averaged Pxy's phase is affected by the window shift?
I've tried to correct this by ensuring that the window shift corresponds to an integer of full phase difference corresponding to 34Hz. Is this correct?
And just a little background about what I am doing:
I basically have numerous time-series pressure measurement over 60 seconds at 1000Hz sampling rate.
Power spectrum analysis indicates that there is a peak frequency at 34 Hz for each signal. (averaged over all windows)
I want to compare each signal's phase difference from each other corresponding to the 34Hz peak.
FFT analysis of individual window reveals that this peak frequency moves around. So I am not sure if cpsd is the correct way to be going about this.
I am currently considering trying to use xcorr to calculate the overall time lag between the signals and then calculate the phase difference from that. I have also heard of hilbert transform, but I got no idea how that works yet.
Yes, cpsd works.
You can test your result by set two input signals, such as:
t=[0:0.001:5];
omega=25;
x1=sin(2*pi*omega*t);
x2=sin(2*pi*omega*t+pi/3);
you can check whether the phase shift calculated by cpsd is pi/3.
I have two sensors seperated by some distance which receive a signal from a source. The signal in its pure form is a sine wave at a frequency of 17kHz. I want to estimate the TDOA between the two sensors. I am using crosscorrelation and below is my code
x1; % signal as recieved by sensor1
x2; % signal as recieved by sensor2
len = length(x1);
nfft = 2^nextpow2(2*len-1);
X1 = fft(x1);
X2 = fft(x2);
X = X1.*conj(X2);
m = ifft(X);
r = [m(end-len+1) m(1:len)];
[a,i] = max(r);
td = i - length(r)/2;
I am filtering my signals x1 and x2 by removing all frequencies below 17kHz.
I am having two problems with the above code:
1. With the sensors and source at the same place, I am getting different values of 'td' at each time. I am not sure what is wrong. Is it because of the noise? If so can anyone please provide a solution? I have read many papers and went through other questions on stackoverflow so please answer with code along with theory instead of just stating the theory.
2. The value of 'td' is sometimes not matching with the delay as calculated using xcorr. What am i doing wrong? Below is my code for td using xcorr
[xc,lags] = xcorr(x1,x2);
[m,i] = max(xc);
td = lags(i);
One problem you might have is the fact that you only use a single frequency. At f = 17 kHz, and an estimated speed-of-sound v = 340 m/s (I assume you use ultra-sound), the wavelength is lambda = v / f = 2 cm. This means that your length measurement has an unambiguity range of 2 cm (sorry, cannot find a good link, google yourself). This means that you already need to know your distance to better than 2 cm, before you can use the result of your measurement to refine the distance.
Think of it in another way: when taking the cross-correlation between two perfect sines, the result should be a 'comb' of peaks with spacing equal to the wavelength. If they overlap perfectly, and you displace one signal by one wavelength, they still overlap perfectly. This means that you first have to know which of these peaks is the right one, otherwise a different peak can be the highest every time purely by random noise. Did you make a plot of the calculated cross-correlation before trying to blindly find the maximum?
This problem is the same as in interferometry, where it is easy to measure small distance variations with a resolution smaller than a wavelength by measuring phase differences, but you have no idea about the absolute distance, since you do not know the absolute phase.
The solution to this is actually easy: let your source generate more frequencies. Even using (band-limited) white-noise should work without problems when calculating cross-correlations, and it removes the ambiguity problem. You should see the white noise as a collection of sines. The cross-correlation of each of them will generate a comb, but with different spacing. When adding all those combs together, they will add up significantly only in a single point, at the delay you are looking for!
White Noise, Maximum Length Sequency or other non-periodic signals should be used as the test signal for time delay measurement using cross correleation. This is because non-periodic signals have only one cross correlation peak and there will be no ambiguity to determine the time delay. It is possible to use the burst type of periodic signals to do the job, but with degraded SNR. If you have to use a continuous periodic signal as the test signal, then you can only measure a time delay within one period of the periodic test signal. This should explain why, in your case, using lower frequency sine wave as the test signal works while using higher frequency sine wave does not. This is demonstrated in these videos: https://youtu.be/L6YJqhbsuFY, https://youtu.be/7u1nSD0RlwY .
I am having a samll problem while converting a spectrum to a time series. I have read many article sand I htink I am applying the right procedure but I do not get the right results. Could you help to find the error?
I have a time series like:
When I compute the spectrum I do:
%number of points
nPoints=length(timeSeries);
%time interval
dt=time(2)-time(1);
%Fast Fourier transform
p=abs(fft(timeSeries))./(nPoints/2);
%power of positive frequencies
spectrum=p(1:(nPoints/2)).^2;
%frequency
dfFFT=1/tDur;
frequency=(1:nPoints)*dfFFT;
frequency=frequency(1:(nPoints)/2);
%plot spectrum
semilogy(frequency,spectrum); grid on;
xlabel('Frequency [Hz]');
ylabel('Power Spectrum [N*m]^2/[Hz]');
title('SPD load signal');
And I obtain:
I think the spectrum is well computed. However now I need to go back and obtain a time series from this spectrum and I do:
df=frequency(2)-frequency(1);
ap = sqrt(2.*spectrum*df)';
%random number form -pi to pi
epsilon=-pi + 2*pi*rand(1,length(ap));
%transform to time series
randomSeries=length(time).*real(ifft(pad(ap.*exp(epsilon.*i.*2.*pi),length(time))));
%Add the mean value
randomSeries=randomSeries+mean(timeSeries);
However, the plot looks like:
Where it is one order of magnitude lower than the original serie.
Any recommendation?
There are (at least) two things going on here. The first is that you are throwing away information, and then substituting random numbers for that information.
The FFT of a real sequence is a sequence of complex numbers consisting of a real and imaginary part. Converting those numbers to polar form gives you magnitude and phase angle. You are capturing the magnitude part with p=aps(fft(...)), but you are not capturing the phase angle (which would involve atan2(...)). You are then making up random numbers (epsilon=...) and using those to replace the original numbers when you reconstruct your time-series. Also, as the FFT of a real sequence has a particular symmetry, substituting random numbers for the phase angle destroys that symmetry, which means that the IFFT will in general no longer be a real sequence, but a sequence of complex numbers - and again, you're only looking at the real portion of the IFFT, so you're throwing away information again. If this is an audio signal, the results may sound somewhat like the original (or they may be completely different), but the waveform definitely won't match...
The second issue is that in many implementations, ifft(fft(...)) will scale the result by the number of points in the signal. There are several different ways to avoid that, with differing results, but sometimes more attractive in different scenarios, depending on what you are trying to do. You can either scale the fft() result before you do the ifft(), or scale the ifft() result at the end, or in some cases, I've even seen both being scaled by a factor of sqrt(N) - doing it twice has the end result of scaling the final result by N, but it is a bit less efficient since you do the scaling twice...
I am trying to compare two data sets in MATLAB. To do this I need to filter the data sets by Fourier transforming the data, filtering it and then inverse Fourier transforming it.
When I inverse Fourier transform the data however I get a spike at either end of the red data set (picture shows the first spike), it should be close to zero at the start, like the blue line. I am comparing many data sets and this only happens occasionally.
I have three questions about this phenomenon. First, what may be causing it, secondly, how can I remedy it, and third, will it affect the data further along the time series or just at the beginning and end of the time series as it appears to from the picture.
Any help would be great thanks.
When using DFT you must remember the DFT assumes a Periodic Signal (As a Superposition of Harmonic Functions).
As you can see, the start point is exact continuation of the last point in harmonic function manner.
Did you perform any Zero Padding in the Spectrum Domain?
Anyhow, Windowing might reduce the Overshooting.
Knowing more about the filter and the Original data might be helpful.
If you say spike near zero frequencies, I answer check the DC component.
You seem interested by the shape, so doing
x = x - mean(x)
or
x -= mean(x)
or
x -= x.mean()
(I love numpy!)
will just constrain the dataset to begin with null amplitude at zero-frequency and to go ahead with comapring the spectra's amplitude.
(as a side-note: did you check that you approprately use fftshift and ifftshift? this has always been the source of trouble for me)
Could be the numerical equivalent of Gibbs' phenomenon. If that's correct, there's no way to remedy it except for filtering.