Digital filter with lowpass, then filtfilt: different output
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Hello world!
Here I am again dealing with digital filters; I am using the latest MATLAB version (R2020a) with DSP system toolbox.
I am manipulating accelerometers data that need to be filtered by a lowpass filter: satisfactory results have been achieved with function lowpass. Since next acquistions have to pass through the very same filter, I extracted the filter built in lowpass writing
[dataflt, Hd] = lowpass(data, cut_off, fs, 'ImpulseResponse', 'fir');
Subsequent steps would be to apply Hd via filtfilt function to all the other acquisitions: as stated in past MathWorks questions, lowpass inherently uses filtfilt function. In order to double-check this statement, I filtered the data used in lowpass with filter Hd writing
dataflt = filtfilt(Hd,data);
I plotted the two outputs to compare them (in the time domain, figure below): it can be concluded that discrepancies arise at the beginning and at the end of the time history.
Why does such a difference exist? Am I neglecting anything fundamental? Is there any way to solve this problem?
I would prefer to use filtfilt (or similar) instead of lowpass because it seems to me the faster way to filter data, given that the filter is precalculated: is this statement true?
Thank you in advance for your wothwhile help.
Riccardo
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/292798/image.png)
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Star Strider
il 13 Mag 2020
Occasionally, filter transients can appear, such as those you are seeing. The way I deal with those (to my satisfaction at least) is to add a vector of ones multiplied by the initial value of the vector to the beginning (and similarly at the end if necessary) to the vector. I then remove these elements from the filtered signal. The length of the added elements can vary, I usually use 10 samples. Experiment to get the result you want.
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Paul
il 8 Giu 2024
lowpass calls filtfilt (or executes code equivalent to filtfilt) only if the underlying filter is IIR. If it's FIR, as in the question by @Riccardo Sorvillo, then lowpass "compensates for the delay introduced by the filter" by using the known delay of a FIR filter that depends on its order.
Example:
rng(100);
fs = 1e3;
t = 0:1/fs:1;
x = [1 2]*sin(2*pi*[50 250]'.*t) + randn(size(t))/10;
% lowpass() with IIR filter, same result as filtfilt()
[ylp,d] = lowpass(x,200,fs,'ImpulseResponse','iir','Steepness',0.5);
yff = filtfilt(d,x);
figure
plot(t,ylp-yff)
% lowpass() with FIR filter, different result than filtfilt()
[ylp,d] = lowpass(x,150,fs);
yff = filtfilt(d,x);
figure
plot(t,ylp-yff),grid
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