MATLAB: Should I use 1-D digital filtering or lowpass filter

filtersignalsignal processing

Hi All

which of the two is better for random signals ( i have 6 signals in 6 degrees of freedom : should I filter them together with the same values of filter ?)

and what does this mean :

lowpass uses a minimum-order filter with a stopband attenuation of 60 dB

It's also not clear how should I calculate wpass for a given random signal !!

with normalized passband frequency wpass in units of π rad/sample

what should I read from my signal to calculate the wpass?

Best Answer

Yes. Also be certain that the signals have uniform sampling intervals. If they do not, use the resample function to force them to be regularly-sampled.

The lowpass function uses the shortest (i.e. most efficient) filter that will give the appropriate results, and the stopband is 60 dB lower than the passband, or 1/1000 the passband amplitude.

EDIT — (6 Apr 2020 at 20:49)

The ‘wpass’ argument is the passband frequency in units of radians/sample, with π being defined as equivalent to the Nyquist frequency (one-half the sampling frequency).

If you use the ‘fs’ argument as well, you can instead designate ‘fpass’ in the same frequency units as ‘fs’ (for example, Hz). The normalisation will be done automatically.

Related Solutions

MATLAB: How to design FIR lowpass filter with cutoff frequency, fc1 = 20 Hz and FIR bandpass filter with cutoff frequency fc2 = 10 Hz and fc3 = 35 Hz.

Déjà vu

how to design IIR highpass filter with cutoff frequency of 20Hz and FIR bandpass filter with cutoff frequency of 10Hz and 15Hz?

Make the appropriate changes to get the cascaded filter you want.

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.)

Best Answer

Related Solutions

MATLAB: How to design FIR lowpass filter with cutoff frequency, fc1 = 20 Hz and FIR bandpass filter with cutoff frequency fc2 = 10 Hz and fc3 = 35 Hz.

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

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