I have a signal in matlab and what to calculate the instantaneous phase for a specific band. I want to filter the signal into this range (using a bandpass filter) and then get the instantaneous phase. I know that there are problems using some filters with non-linear phase responses, is there any way to get around this? I have found some information online about back filtering the signal but it's still a little unclear. I'd like to avoid using wavelets (they're probably overkill here). Thanks.
Unless you resort to noncasual techniques (like the filtfilt suggested in the comment by nibot), you will always have some phase distortion. Linear phase FIRs with a delay D will add a phase of 2*pi*f*D, while nonlinear phase IIRs will add phase that is not linearly dependent on f.
In both cases, it is easy to compute the phase distortion (for example, use freqz(num, den) for IIRs) and account for that distortion when interpreting the resulting measurement. Of course, you'll have meaningless results when the phase changes significantly over the frequency range you are interested in - but that's a different issue.
Related
I have audio record.
I want to detect sinusoidal pattern.
If i do regular fft i have result with bad SNR.
for example
my signal contents 4 high frequencies:
fft result:
To reduce noise i want to do Coherent integration as described in this article: http://flylib.com/books/en/2.729.1.109/1/
but i cant find any MATLAB examples how to do it. Sorry for bad english. Please help )
I look at spectra almost every day, but I never heard of 'coherent integration' as a method to calculate one. As also mentioned by Jason, coherent integration would only work when your signal has a fixed phase during every FFT you average over.
It is more likely that you want to do what the article calls 'incoherent integration'. This is more commonly known as calculating a periodogram (or Welch's method, a slightly better variant), in which you average the squared absolute value of the individual FFTs to obtain a power-spectral-density. To calculate a PSD in the correct way, you need to pay attention to some details, like applying a suitable Fourier window before doing each FFT, doing the proper normalization (so that the result is properly calibrated in i.e. Volt^2/Hz) and using half-overlapping windows to make use of all your data. All of this is implemented in Matlab's pwelch function, which is part of the signal-processing toolbox. See my answer to a similar question about how to use pwelch.
Integration or averaging of FFT frames just amounts to adding the frames up element-wise and dividing by the number of frames. Since MATLAB provides vector operations, you can just add the frames with the + operator.
coh_avg = (frame1 + frame2 + ...) / Nframes
Where frameX are the complex FFT output frames.
If you want to do non-coherent averaging, you just need to take the magnitude of the complex elements before adding the frames together.
noncoh_avg = (abs(frame1) + abs(frame2) + ...) / Nframes
Also note that in order for coherent averaging to work the best, the starting phase of the signal of interest needs to be the same for each FFT frame. Otherwise, the FFT bin with the signal may add in such a way that the amplitudes cancel out. This is usually a tough requirement to ensure without some knowledge of the signal or some external triggering so it is more common to use non-coherent averaging.
Non-coherent integration will not reduce the noise power, but it will increase signal to noise ratio (how the signal power compares to the noise power), which is probably what you really want anyway.
I think what you are looking for is the "spectrogram" function in Matlab, which computes the short time Fourier transform(STFT) of an input signal.
STFT
Spectrogram
I am new to matlab and signal processing methods, but i am trying to use its filter properties over a set of data I have. I have a collection of amplitude values obtained at different timestamps. When this is plotted, I get a waveform with several peaks that I can identify. I then perform calculations to derive the time between each consecutive peak and I want to eliminate the rates that are around the range of 48-52peaks per second.
What would be the correct way to go about processing this data step by step? Would a bandstop or notch filter be better if I want to eliminate those frequencies and not attenuate it simply? I am completely lost in the parameters required to feed into the filters for this. Please help...
periodogram is OK, but I would suggest using pwelch instead. It makes a more reasonable PSD estimate and the default parameters are well thought out (Hann windows, 50% overlap of segments, etc.)
If what you want is to remove signals in a wide band (e.g. 48-52 Hz) equally, rather than a single and unchanging frequency, than a bandstop filter is ideal. For example:
fs = 2048;
y = rand(fs*8, 1);
[b,a] = ellip(4, 2, 40, [46 54]/(fs/2));
yy = filter(b,a,y);
This will use a 4th order elliptic bandstop filter to filter the random data variable 'y'. filtfilt.m is also a nice function; it applies the filter forwards and backwards so you get twice the filter action and none of the phase lag or dispersion.
I am currently doing something similar to what you are doing.
I am processing a lot of signals from the Inertial Measurement Unit and motor drives. They all are asynchronously obtained, i.e. they all have very different timestamp and also very different acquisition frequency.
First thing I did was to interpolate all signals data in order to have all signals with same timestamp. You can use the matlab function interp to do this.
After this, you will have all signals with same sample frequency and also timestamp, which will be good in further analysis.
Ok, another thing you can do to analyse the frequency of the peaks is to perform the fourier transform. For beginners i advice the use of periodogram function and not the fft function.
Imagine you signal is x and your sample frequency (after interpolation) is Fs.
You can now use the function periodogram available in matlab like this:
[P,f] = periodogram(x,[],[length(t)],Fs);
This will give the power vs frequency of your signal. After that you will be able to plot and take a look at the frequencies of your signal. In other words, you be able to see the frequencies of the signals that make your acquired signal.
Plot the data this way:
plot(f,P); or semilogy(f,P);
The second is the same thing as the first, but with a logarithmic scale.
After this analysis you can use the Filter Desing and Analysis Tool to design you filter. Just type fdatool in matlab and it will open the design window. Choose the filter type, the cut and pass frequencies and click in design. This tool is very intuitive.
After designing you can export the filter to workspace.
Finally you can use the filter you designed in your signal to see if its what you wanted.
Use the functions filter os filtfilt for this.
Search in the web of the matlab help for the functions I wrote to get more details.
There are a lot of examples availables too.
I hope I could help you.
Good luck.
I've got 129 samples of some signal. I have to discover what signal it is (what components it has) and try to reconstruct it.
so after I have done FFT i wanted to find phase of signal so i try:
phs=unwrap(angle(y));
pha_wek=(0:length(y)-1)'/length(y)*129;
plot(pha_wek,phs)
Result I've got it's not what I expected. Can you point me what is wrong ?
The plot looks exactly right to me, depending upon what the original signal is. The discontinuities in the phase angle are due to zero-crossings in the magnitude. However, the phase jumps are less than pi (which is what you might expect if the input signal were originally real and had been windowed when applying the FFT). So my guess is that this signal is some form of complex modulation with some discrete frequencies.
Generally viewing the phase tells you a lot less about the signal than its magnitude. It is essential for reconstructing the signal, but much harder to interpret (particularly as it is subject to dramatic changes with a simple shift in the time domain).
You might be better plotting the phase without using the unwrap command In this case, the issue is the unwrap command - its aim is to limit jumps in phase to be less than pi, which is why the final phase is around -4pi.
I'm not sure what your challenge is, but it isn't clear what you mean by reconstructing the samples. You've already got these, so what's the issue? (Are you being asked to reconstruct interpolated samples)?
In matlab we can use filtfilt function to filter out data which implements forward and backward filtering techniques which results in zero-phase. But it's difficult to implement this filter in real time as it involves backward filtering.
I want to implement a 1st order high-pass or low pass filter with zero phase in real time. How can i achieve this?
I have search the web for days but unable to get any clue to start with it!
Thanks in advance!
It is not possible to perform a zero-phase filter in real-time because a zero-phase filter requires filter coefficients that are symmetric around zero. That means that the filter is non-causal, or that current output depends on future input. This of course is not possible in the real-time case and can be faked as in the case of filtfilt during post-processing.
What you probably are looking for is a linear phase filter. Don't let the name confuse you; this does not mean that the filter produces any phase distortion. It only means that a time shift is applied to the output. A linear phase shift with respect to frequency results in a constant shift with respect to time. So basically your output will be delayed some constant number of samples (group delay) from the input.
So the only difference between a zero-phase and linear-phase filter is that the linear-phase filter output is a delayed version of the zero-phase output. This delay can be accounted for by keeping track of the group delay if you need to keep the output aligned in time with the input.
Response to comment:
FIR filters are guaranteed to be linear phase if their coefficients are symmetric about the center. MATLAB can easily create these types of filters with functions such as fir1 or firpm. The examples in the documentation of those functions should show you how to use them.
The group delay of a linear phase FIR filter is (L-1)/2 where L is the length of the filter. Because of this and a few other things, I would usually choose an odd filter length so the delay is aligned to a sample and not in between samples. This basically means that the output signal will be delayed from the input by (L-1)/2 samples.
Implementing the actual filtering process is basically discrete convolution of the input with the filter. This involves reversing the filter coefficients, multiplying them by the most recent L input samples, and adding those results to produce a single output sample. Then a new input sample is brought in and the whole process is done again to produce another sample (mutiplying and summing over a sliding window). You should be able to find some sample code for convolution on the web.
This is the direct way to perform FIR filtering, but for longer filters, it may be more efficient to perform fast convolution with an FFT. This will be a lot more difficult to get right, so unless you are talking about high sample rates and long filters, I would just go with the direct approach.
A "non-causal" zero-phase filter plus a sufficient amount of added delay can be approximated by a causal linear phase FIR filter. This assumes that adding some delay fits your system requirements.
In your case, you could take the impulse response of your forward+backward asymmetric (or non-linear phase) filtering process, window that impulse response to make the response finite in length, delay the finite length kernel so that it doesn't require "future" samples, and use that as a FIR filter kernel. You will have to check the results to see if your chosen window was appropriate in length and shape. There may be a trade-off in choosing a delay versus quality of the filter due to the finite length windowing required for that delay.
Does anyone know how to use filters in MATLAB?
I am not an aficionado, so I'm not concerned with roll-off characteristics etc — I have a 1 dimensional signal vector x sampled at 100 kHz, and I want to perform a high pass filtering on it (say, rejecting anything below 10Hz) to remove the baseline drift.
There are Butterworth, Elliptical, and Chebychev filters described in the help, but no simple explanation as to how to implement.
There are several filters that can be used, and the actual choice of the filter will depend on what you're trying to achieve. Since you mentioned Butterworth, Chebyschev and Elliptical filters, I'm assuming you're looking for IIR filters in general.
Wikipedia is a good place to start reading up on the different filters and what they do. For example, Butterworth is maximally flat in the passband and the response rolls off in the stop band. In Chebyschev, you have a smooth response in either the passband (type 2) or the stop band (type 1) and larger, irregular ripples in the other and lastly, in Elliptical filters, there's ripples in both the bands. The following image is taken from wikipedia.
So in all three cases, you have to trade something for something else. In Butterworth, you get no ripples, but the frequency response roll off is slower. In the above figure, it takes from 0.4 to about 0.55 to get to half power. In Chebyschev, you get steeper roll off, but you have to allow for irregular and larger ripples in one of the bands, and in Elliptical, you get near-instant cut off, but you have ripples in both bands.
The choice of filter will depend entirely on your application. Are you trying to get a clean signal with little to no losses? Then you need something that gives you a smooth response in the passband (Butterworth/Cheby2). Are you trying to kill frequencies in the stopband, and you won't mind a minor loss in the response in the passband? Then you will need something that's smooth in the stop band (Cheby1). Do you need extremely sharp cut-off corners, i.e., anything a little beyond the passband is detrimental to your analysis? If so, you should use Elliptical filters.
The thing to remember about IIR filters is that they've got poles. Unlike FIR filters where you can increase the order of the filter with the only ramification being the filter delay, increasing the order of IIR filters will make the filter unstable. By unstable, I mean you will have poles that lie outside the unit circle. To see why this is so, you can read the wiki articles on IIR filters, especially the part on stability.
To further illustrate my point, consider the following band pass filter.
fpass=[0.05 0.2];%# passband
fstop=[0.045 0.205]; %# frequency where it rolls off to half power
Rpass=1;%# max permissible ripples in stopband (dB)
Astop=40;%# min 40dB attenuation
n=cheb2ord(fpass,fstop,Rpass,Astop);%# calculate minimum filter order to achieve these design requirements
[b,a]=cheby2(n,Astop,fstop);
Now if you look at the zero-pole diagram using zplane(b,a), you'll see that there are several poles (x) lying outside the unit circle, which makes this approach unstable.
and this is evident from the fact that the frequency response is all haywire. Use freqz(b,a) to get the following
To get a more stable filter with your exact design requirements, you'll need to use second order filters using the z-p-k method instead of b-a, in MATLAB. Here's how for the same filter as above:
[z,p,k]=cheby2(n,Astop,fstop);
[s,g]=zp2sos(z,p,k);%# create second order sections
Hd=dfilt.df2sos(s,g);%# create a dfilt object.
Now if you look at the characteristics of this filter, you'll see that all the poles lie inside the unit circle (hence stable) and matches the design requirements
The approach is similar for butter and ellip, with equivalent buttord and ellipord. The MATLAB documentation also has good examples on designing filters. You can build upon these examples and mine to design a filter according to what you want.
To use the filter on your data, you can either do filter(b,a,data) or filter(Hd,data) depending on what filter you eventually use. If you want zero phase distortion, use filtfilt. However, this does not accept dfilt objects. So to zero-phase filter with Hd, use the filtfilthd file available on the Mathworks file exchange site
EDIT
This is in response to #DarenW's comment. Smoothing and filtering are two different operations, and although they're similar in some regards (moving average is a low pass filter), you can't simply substitute one for the other unless it you can be sure that it won't be of concern in the specific application.
For example, implementing Daren's suggestion on a linear chirp signal from 0-25kHz, sampled at 100kHz, this the frequency spectrum after smoothing with a Gaussian filter
Sure, the drift close to 10Hz is almost nil. However, the operation has completely changed the nature of the frequency components in the original signal. This discrepancy comes about because they completely ignored the roll-off of the smoothing operation (see red line), and assumed that it would be flat zero. If that were true, then the subtraction would've worked. But alas, that is not the case, which is why an entire field on designing filters exists.
Create your filter - for example using [B,A] = butter(N,Wn,'high') where N is the order of the filter - if you are unsure what this is, just set it to 10. Wn is the cutoff frequency normalized between 0 and 1, with 1 corresponding to half the sample rate of the signal. If your sample rate is fs, and you want a cutoff frequency of 10 Hz, you need to set Wn = (10/(fs/2)).
You can then apply the filter by using Y = filter(B,A,X) where X is your signal. You can also look into the filtfilt function.
A cheapo way to do this kind of filtering that doesn't involve straining brain cells on design, zeros and poles and ripple and all that, is:
* Make a copy of the signal
* Smooth it. For a 100KHz signal and wanting to eliminate about 10Hz on down, you'll need to smooth over about 10,000 points. Use a Gaussian smoother, or a box smoother maybe 1/2 that width twice, or whatever is handy. (A simple box smoother of total width 10,000 used once may produce unwanted edge effects)
* Subtract the smoothed version from the original. Baseline drift will be gone.
If the original signal is spikey, you may want to use a short median filter before the big smoother.
This generalizes easily to 2D images, 3D volume data, whatever.