jainsanket
Newbie level 2
Hello Experts,
The output of the FIR filters is convolution of the input signal and the filter kernel. In that case, the length of the output signal should be greater than input signal by M-1 points where M is the length of the filter kernel.
x=ecg(500)'+0.25*randn(500,1); %noisy waveform
h=fdesign.lowpass('Fp,Fst,Ap,Ast',0.15,0.2,1,60);
d=design(h,'equiripple'); %Lowpass FIR filter
y=filtfilt(d.Numerator,1,x); %zero-phase filtering
y1=filter(d.Numerator,1,x); %conventional filtering
In the above code, the length of the output is same as the length of my input signal even though I have implemented FIR filtering.
Can someone explain the reason of same length of the output signal? I expected my output signal to be greater than input signal.
Does MATLAB use convolution for filtering?
The output of the FIR filters is convolution of the input signal and the filter kernel. In that case, the length of the output signal should be greater than input signal by M-1 points where M is the length of the filter kernel.
x=ecg(500)'+0.25*randn(500,1); %noisy waveform
h=fdesign.lowpass('Fp,Fst,Ap,Ast',0.15,0.2,1,60);
d=design(h,'equiripple'); %Lowpass FIR filter
y=filtfilt(d.Numerator,1,x); %zero-phase filtering
y1=filter(d.Numerator,1,x); %conventional filtering
In the above code, the length of the output is same as the length of my input signal even though I have implemented FIR filtering.
Can someone explain the reason of same length of the output signal? I expected my output signal to be greater than input signal.
Does MATLAB use convolution for filtering?