A simple FIR filter will probably do what you want. The ‘fcuts’ vector is the vector of stopband and passband edges, so [low_stop low_pass high_pass high_stop]. Sharper cutoffs (less distance between the respective stopband and passband frequencies) create longer filters. If this filter is too long, decrease ‘low_stop’ and increase high_stop until it works with your signal. It is best to make the differences the same on both ends of the filter, but there is no absolute requirement.
Fs = 96E+3; % Sampling Frequency (Hz)
fcuts = [850 891 1122 1163]; % Frequency Vector (Hz)
mags = [0 1 0]; % Magnitude (Defines Passbands & Stopbands)
devs = [0.05 0.01 0.05]; % Allowable Deviations
[n,Wn,beta,ftype] = kaiserord(fcuts,mags,devs,Fs);
n = n + rem(n,2);
hh = fir1(n,Wn,ftype,kaiser(n+1,beta),'scale');
figure(1)
freqz(hh, 1, 2^16, Fs)
set(subplot(2,1,1), 'XLim', [0 1500]) % Set Frequency Axis To Show 0-1500 Hz
set(subplot(2,1,2), 'XLim', [0 1500]) % Set Frequency Axis To Show 0-1500 Hz
s = randn(1, 1E+5); % Gaussian White Noise
t = 0 : 1/Fs : (length(s)-1)/Fs;
sfilt = filtfilt(hh, 1, s); % Filter Signal
figure(2)
subplot(2,1,1)
plot(t, s)
grid
title('Raw Signal')
subplot(2,1,2)
plot(t, sfilt)
grid
title('Filtered Signal')
An alternative IIR design (that will likely produce a shorter filter with similar characteristics) would be a Chebyshev Type II filter. The designfilt function makes that process straightforward.
