Reputation: 3961
I want to numerically calculate several convolutions like
where the x
, y
, z
, w
functions are given in the below code:
t = linspace(-100,100,10000);
x = t.*exp(-t.^2);
y = exp(-4*t.^2).*cos(t);
z = (t-2)/((t-2).^2+3^2);
w = exp(-3*t.^2).*exp(2i*t);
u = conv(conv(conv(x,y),z),w);
plot(t,u) % ??? - if we want to convolute N functions, what range should t span?
Is this the most efficient way to calculate and plot multiple convolutions? Is it generally better to numerically integrate the functions for each convolution?
Edit:
This is the plot of the real part of my convolution, u
vs t
:
whereas the method (using FFTs) suggested by a poster below gives me:
What causes this discrepancy?
Upvotes: 2
Views: 701
Reputation: 1389
If the signal length is long, fft method would be better.
Below is an example.
t = linspace(-100,100,10000);
x = t.*exp(-t.^2);
y = exp(-4*t.^2).*cos(t);
z = (t-2)/((t-2).^2+3^2);
w = exp(-3*t.^2).*exp(2i*t);
L_x=fft(x);
L_y=fft(y);
L_z=fft(z);
L_w=fft(w);
L_u=L_x.*L_y.*L_z.*L_w; %convolution on frequency domain
u=ifft(L_u);
figure(1)
plot(t,abs(u))
figure(2)
plot(t,real(u))
figure(3)
plot(t,imag(u))
Upvotes: 2