Fitting real data curve to a homogeneous diff
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Hello! I measured accelerations with matlab Mobile and I encountered the problem that the time step was not homogeneous and therefore the measured frequency. Therefore, I created a homogeneous diff (blue curve) and I would like to find a code in MATLAB that adapts the measured function with the real values (red curve) to that homogeneous time increment.
I attach a picture of the problem.
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Star Strider
2023-5-16
1 个投票
8 个评论
Daniel Sánchez Muñoz
2023-5-23
Star Strider
2023-5-23
Without having the data to work with, I cannot add anything to what I wrote earlier.
Daniel Sánchez Muñoz
2023-5-23
Star Strider
2023-5-24
Without having the data to work with, I cannot add anything to what I wrote earlier.
Daniel Sánchez Muñoz
2023-5-24
I am not certain what result you want.
This resamples all of them to a common sampling frequency. There are some obvious differences between the original and resampled signals.
The resample function has a number of options, including the interplation method (I chose the default 'linear' method for all of these). You can slso choose a different sampling frequency (I chose 10 Hz here because it is closest to the actual sampling frequency), although that may create data where no data previously existed, so I advise against it.
LD = load('Prueba.mat')
Acceleration = LD.Acceleration
VN = Acceleration.Properties.VariableNames;
[h,m,s] = hms(Acceleration.Timestamp);
s
% X = Acceleration.X
% Y = Acceleration.Y
% Z = Acceleration.Z
format long
ds = diff(s);
ds_stats = [min(ds); max(ds); mean(ds); std(ds); 1/mean(ds)] % 'Original' Time Vector Statistics
Fs = ds_stats(end)
Fsc = ceil(Fs)
Accelerationr = resample(Acceleration, Fsc) % 'Accelerationr' Is The Resampled 'Acceleration' 'timetable'
[h,m,s] = hms(Accelerationr.Time);
ds = diff(s);
ds_stats = [min(ds); max(ds); mean(ds); std(ds); 1/mean(ds)] % 'Resampled' Time Vector Statistics
figure
tiledlayout(3,1)
for k = 1:3
nexttile
plot(Acceleration.Timestamp, Acceleration{:,k})
title(VN{k})
grid
ylim('padded')
end
sgtitle('Original')
figure
tiledlayout(3,1)
for k = 1:3
nexttile
plot(Accelerationr.Time, Accelerationr{:,k})
grid
title(VN{k})
ylim('padded')
end
sgtitle('Resampled')
figure
tiledlayout(3,1)
for k = 1:3
nexttile
plot(Acceleration.Timestamp, Acceleration{:,k}, 'DisplayName','Original')
hold on
plot(Accelerationr.Time, Accelerationr{:,k}, 'DisplayName','Resampled')
hold off
grid
title(VN{k})
if k == 1
legend('Location','best')
end
ylim('padded')
end
sgtitle('Original Co-Plotted With Resampled')
figure
k = 2;
plot(Acceleration.Timestamp, Acceleration{:,k}, 'DisplayName','Original')
hold on
plot(Accelerationr.Time, Accelerationr{:,k}, 'DisplayName','Resampled')
hold off
grid
title(VN{k})
legend('Location','best')
I am not certain what part of what signal is in the originally provided plot image, or I would post it in detail here.
.
Daniel Sánchez Muñoz
2023-5-28
Star Strider
2023-5-28
I would appreciate it if you would accept my answer.
I actually use the most common frequency, that I derive from the differences in the sampling intervals. I use mean, I also could have used mode.
I notice the the code you posted uses ‘fs’ to define ‘dt’ without first defining ‘fs’. I have no idea how you define ‘fs’.
I also use resample instead of interp1 because resample uses an anti-aliasing filter. This is important for signal processing applications, and signal processing is apprarently what you want to do.
The resample function also has the 'spline' option as an interpolation method. I rarely use it (preferring 'pchip') because it can give some wildly varying results.
.
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