Vectorize array of filters to avoid for-loop for different filters

Hi,

I need to use different filters for data. Each filter is applied to a 2D array, one dimension is time vector, the other dimension is the cases we want to apply filter. I have to run for-loop for different filters. Is there a faster way for the implementation as usually we vectorize to matrix operation to make it faster. The simplified code for representing the problem using for-loop is as below.

Thanks

clear all;
% Create a 2D-array for two vectors of data vs. time
time = [0:1e-3:10]';
x = [sin(time)+0.2*randn(size(time)),1.5*sin(time)+0.3*randn(size(time))];
% There are two filters
[b_filt(1,:),a_filt(1,:)] = butter(2,0.1);
[b_filt(2,:),a_filt(2,:)] = butter(2,0.2);
% Scan through all filters
for index_filter=1:size(b_filt,1)
        % for each filter, apply filter to the data, along time axis, which is the first axis i.e. row
   x_filtered(index_filter,:,:) = filter(b_filt(index_filter,:),a_filt(index_filter,:),x);
end
% Plot the result for one case for illustration
x_plot = x(:,1);
x_filt_plot = x_filtered(1,:,1);
figure;
hold on;
plot(time,x_plot(:),'b');
plot(time,x_filt_plot(:),'r');
legend('Pre-filtered','Filtered');

[Peter]

I'm looking for the same solution!

[Mathieu NOE]

you could put all your filters is serie (either by using series or making the convolution of the filter coefficients) and then run the time simulation only once