MATLAB: How to to filter non-uniformly sampled data

filtering non-uniformly sampling

I've a series of force sensor data acquired by an industrial robot controller. Sadly the data has to be read out in a background task, so the sampling time differs between 1m and 20ms. Due to high oscillation in the process, I have to low-pass filter these force values.

What is the best way to filter this data? Right now, I'm interpolating linear between the data points and than resample with a sampling rate of 1kHz (1ms sampling time). Then I'm using a moving average filter to smooth and filter these data.

But I'm not happy with this method because I neither know how this is changing the validity of my data.

Help is highly appreciated. Thank you.

Best Answer

Your approach is correct. Rather than using interp1, use the Signal Processing Toolbox resample (link) function. It incorporates an anti-aliasing filter, and is preferred for signal processing purposes.

A useful lowpass filter design prototype is:

Fs = 360;                                                   % Sampling Frequency (Hz)
Fn = Fs/2;                                                  % Nyquist Frequency (Hz)
Wp = 100/Fn;                                                % Passband Frequency (Normalised)
Ws = 101/Fn;                                                % Stopband Frequency (Normalised)
Rp =   1;                                                   % Passband Ripple (dB)
Rs = 150;                                                   % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                             % Filter Order
[z,p,k] = cheby2(n,Rs,Ws);                                  % Filter Design
[soslp,glp] = zp2sos(z,p,k);                                % Convert To Second-Order-Section For Stability
figure(3)
freqz(soslp, 2^16, Fs)                                      % Filter Bode Plot
filtered_signal = filtfilt(soslp, glp, original_signal);    % Filter Signal

Make appropriate changes for your signal.

Related Solutions

MATLAB: How to Butterworth Filter with Bandpass [10 500] with sampling rate 1000

You have to divide your passband and stopband frequencies by the Nyquist frequency to normalise them:

Fs = 1000;                                          % Sampling Frequency
Fn = Fs/2;                                          % Nyquist Frequency
Wp = [10  495]/Fn;                                  % Norrmalised Passband Frequencies
Ws = [5   499]/Fn;                                  % Normalised Stopband Frequencies
Rp =  1;                                            % Passband Ripple (dB)
Rs = 25;                                            % Stopband Ripple (dB)
[n,Wn] = buttord(Wp, Ws, Rp, Rs);                   % Optimal Filter Order
[b,a] = butter(n, Wn);                              % Calculate Filter Coefficients
[sos,g] = tf2sos(b,a);                              % Convert To Second-Order Sections For Stability
figure(1)
freqz(sos, 4096, Fs)                                % Filter Bode Plot

You cannot have any frequency exactly at zero or exactly at the Nyquist frequency, so I designed your passband to be close enough to them to be stable. In your filter, you can also use a high-pass design with a lower cutoff of 10 Hz, since your upper passband is at the Nyquist frequency. The passband and stopband ripple values are necessary to specify in the design but irrelevant to a Butterworth filter. These are acceptable values for most filter designs (for example Chebyshev) that require them.

You would use the ‘sos’ and ‘g’ variables in your actual filter:

filtered_signal = filtfilt(sos, g, signal);

(This design works well in R2016a and should also work in earlier releases.)

MATLAB: Convert a signal in a particular frequency range

See my Answer to your other Question for the details.

This filter will be:

Fs = 2000;                                              % Sampling Frequency (Hz)
Fn = Fs/2;                                              % Nyquist Frequency (Hz)
Wp = [20 450]/Fn;                                       % Passband Frequencies (Normalised)
Ws = [19 455]/Fn;                                       % Stopband Frequencies (Normalised)
Rp = 10;                                                % Passband Ripple (dB)
Rs = 50;                                                % Stopband Ripple (dB)
[n,Ws] = cheb2ord(Wp,Ws,Rp,Rs);                         % Filter Order
[z,p,k] = cheby2(n,Rs,Ws);                              % Filter Design
[sosbp,gbp] = zp2sos(z,p,k);                            % Convert To Second-Order-Section For Stability

NOTE — Make appropriate changes to my other code for your EMG signal.

Best Answer

Related Solutions

MATLAB: How to Butterworth Filter with Bandpass [10 500] with sampling rate 1000

MATLAB: Convert a signal in a particular frequency range

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