Non-uniform Discrete Data Sample Filtering

Question

Kerem Asaf Tecirlioglu 2022-8-19

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1782165-non-uniform-discrete-data-sample-filtering

回答： Maximilian Schönau 2022-10-10

calib_data1.mat

Hello,

I have collected some data from my encoder and IMU and I am trying to come up with a calibration function. However the data is quite noisy (especially IMU). I timestamped then numerized the timestamp using datenum function. I would like to have a spline like output.

I have come across this question and trying to utilize the code shared.

D = load('test_Data.mat');
t = D.test_data(:,1);
s = D.test_data(:,2);
Fs = 1;                                                             % Sampling Frequency
Ts = 1;                                                             % Sampling Interval
[sr,tr] = resample(s, t, Fs);                                       % Resample, Return Resampled Signal & New Time Vector
sre = sr(1:end-2);                                                  % Eliminate End Transient
tre = tr(1:end-2);                                                  % Eliminate End Transient
figure
plot(t, s)
hold on
plot(tre, sre, '--')
hold off
grid
legend('Original Signal', 'Resampled Signal')
L = numel(t);                                                       % Signal Length
Fn = Fs/2;                                                          % Nyquist Frequency
sm = sre - mean(sre);                                               % Subtract Mean
FTs = fft(sm)/L;                                                    % Scaled Fourier Transform
Fv = linspace(0, 1, fix(L/2)+1)*Fn;                                 % Frequency Vector
Iv = 1:numel(Fv);                                                   % Index Vector
figure
plot(Fv, abs(FTs(Iv))*2)
grid
title('Fourier Transform')
Wp = [0.05]/Fn;                                                     % Passband Frequency (Normalised)
Ws = [0.09]/Fn;                                                     % Stopband Frequency (Normalised)
Rp =  1;                                                            % Passband Ripple
Rs = 60;                                                            % Passband Ripple (Attenuation)
[n,Wp] = ellipord(Wp,Ws,Rp,Rs);                                     % Elliptic Order Calculation
[z,p,k] = ellip(n,Rp,Rs,Wp,'low');                                  % Elliptic Filter Design: Zero-Pole-Gain 
[sos,g] = zp2sos(z,p,k);                                            % Second-Order Section For Stability
figure
freqz(sos, 2^16, Fs)                                                % Filter Bode Plot
sre_filt = filtfilt(sos, g, sre);                                   % Filter Signal
figure
subplot(2,1,1)
plot(tre, sre)
grid
title('Resampled Signal')
subplot(2,1,2)
plot(tre, sre_filt, '-')
grid
title('Filtered Resampled Signal')

However resampling results singular, a single result; not an array.

3 个评论
显示 1更早的评论隐藏 1更早的评论

Star Strider 2022-8-19

在 MATLAB Online 中打开

It is difficult to determine what to do with these data.

You need to determine the most appropriiate resampling rate and how to handle all the NaN values that result from creating a data set with the constant sampling intervals necesssary for any sort of signal processing. See the documentation on the retime function for details on its options.

I leave it to you to choose how best to work with them —

LD = load(websave('calib_data1','https://www.mathworks.com/matlabcentral/answers/uploaded_files/1102160/calib_data1.mat'))

LD = struct with fields:

enc_time: [77552×1 double] enc_val: [77552×1 double] imu_time: [4514×1 double] imu_val: [4514×1 double]

enc_time = LD.enc_time;

enc_val = LD.enc_val;

DT = datetime(enc_time, 'ConvertFrom','datenum');

DT.Format = 'HH:mm:ss.SSSSSS';

T1 = table(DT,enc_val)

T1 = 77552×2 table

DT enc_val _______________ _______ 16:32:23.674000 0.65 16:32:23.674000 0.65 16:32:23.674000 0.65 16:32:23.674000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65 16:32:23.721000 0.65

TT1 = table2timetable(T1);

Fs = 500; % Resampling Frequency

TT1 = retime(TT1,'regular','SampleRate',Fs)

TT1 = 54120×1 timetable

DT enc_val _______________ _______ 16:32:23.674000 0.65 16:32:23.676000 NaN 16:32:23.678000 NaN 16:32:23.680000 NaN 16:32:23.682000 NaN 16:32:23.684000 NaN 16:32:23.686000 NaN 16:32:23.688000 NaN 16:32:23.690000 NaN 16:32:23.692000 NaN 16:32:23.694000 NaN 16:32:23.696000 NaN 16:32:23.698000 NaN 16:32:23.700000 NaN 16:32:23.702000 NaN 16:32:23.704000 NaN

figure

plot(DT, enc_val)

grid

title('Original')

figure

plot(TT1.DT,TT1.enc_val, '.', 'MarkerSize',0.1)

grid

title(sprintf('After ''retime'' (Fs = %0.1f Hz)',Fs))

The filtering will be relatively straightforward after the data are prepared for it.

Any NaN values in the input vector will result in the entire filttered output being NaN.

.

Kerem Asaf Tecirlioglu 2022-8-24

编辑：Kerem Asaf Tecirlioglu 2022-8-24

在 MATLAB Online 中打开

calib_data1_rev1.mat

I have handled the NaN value by replacing with the nearest integer sample. I am attaching the arrays .

a=1;
q=1;
for i = 1:size(TT1.imu_val)
if isnan(TT1.imu_val(i))
    a = a + 1;
else
    for x = q:i
        TT1.imu_val(x) = TT1.imu_val(i);
    end
    a = 1;
    q = 1 + x;
end
end

I have never used matlab for filtering before. How should I proceed with filtering

Mathieu NOE 2022-8-24

hi

for filtering look for example for smoothdata

请先登录，再进行评论。

请先登录，再回答此问题。

Answer 1

Maximilian Schönau 2022-10-10

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1782165-non-uniform-discrete-data-sample-filtering#answer_1070300

I would reccomend you using the live script task "Smooth Data". There you can graphically try out different filter methods and after that convert your favorite filter to code.

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

请先登录，再进行评论。

Non-uniform Discrete Data Sample Filtering

3 个评论
显示 1更早的评论隐藏 1更早的评论

回答（1 个）

0 个评论
显示 -2更早的评论隐藏 -2更早的评论

另请参阅

类别

标签

产品

版本

Community Treasure Hunt

Non-uniform Discrete Data Sample Filtering

3 个评论 显示 1更早的评论隐藏 1更早的评论

回答（1 个）

0 个评论 显示 -2更早的评论隐藏 -2更早的评论

另请参阅

类别

标签

产品

版本

Community Treasure Hunt

3 个评论
显示 1更早的评论隐藏 1更早的评论

0 个评论
显示 -2更早的评论隐藏 -2更早的评论