MATLAB: Anti-aliasing filter after sampling

anti-aliasingsampling

can I applied an anti-aliasing filter to the signal after sampling; and how to do this

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As a general rule, anti-aliasing filters are implemented in hardware (as analogue filters) before sampling. An aliased signal in your sampled signal will always be part of it.

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MATLAB: A signal whose sampling rate is 22050 Hz but what will happen if we sample it at 32KHz,44.1KHz and 48 KHz

If you resampled your signal at a higher rate, you would likely not have any detectable difference in the output. The hardware in most ADCs incorporates a hardware anti-aliasing filter, so any frequencies above 11025 Hz should not be present in the original sampled signal. The only significant effect would be to lengthen the signal vector and the file size.

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.)

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Related Solutions

MATLAB: A signal whose sampling rate is 22050 Hz but what will happen if we sample it at 32KHz,44.1KHz and 48 KHz

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

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