The Definition
The integral
∫g(t−τ)h(τ)dτ=g⊗h
is called the convolution of g and h. Here the ⊗ denotes
convolution, it is sometimes represented by a ⋆. This is representing the
amount of overlap of function h as g is shifted over it. It in effect
blends one function with another. This crops up a lot in physics and in
statistics.
The Fourier Transform of a Convolution
If F(u) is the Fourier transform of f(x) and G(u) is the Fourier
transform of g(x), multiplying both Fourier transforms together we have
F(u)G(u)=∫∞−∞f(x)e−2πiuxdx∫∞−∞g(x)e−2πiuxdx
So that we can rewrite this as a double integral we replace the x with
two dummy variables and rewrite as
F(u)G(u)=∫∞−∞f(τ)e−2πiuτdτ∫∞−∞g(υ)e−2πiuυdυ
=∫∞−∞∫∞−∞e−2πiu(τ+υ)f(τ)g(υ)dτdυ
Next, making a change of variables so x=τ+υ,dx=dυ
F(u)G(u)=∫∞−∞∫∞−∞f(τ)g(x−τ)dτdx
=∫∞−∞e−i2πx[∫∞−∞f(τ)g(x−τ)dτ]dx
=∫∞−∞e−i2πxf(x)⊗g(x)dx
i.e., F(u)G(u) and f(x)⊗g(x) are Fourier transform pairs. This
result is very important in optical processing, and signal processing in
general. It means a convolution can be calculated by Fourier transforming
both signals, multiplying the result and inverse transforming back. This is
often quicker than calculating the convolution directly and can be performed
using a simple system of lenses and filters.
Properties
Commutativity
f⊗g=g⊗f
Associativity
f⊗(g⊗h)=(f⊗g)⊗h
Distributivity
f⊗(g+h)=(f⊗g)+(f⊗h)
Scalar multiplication
a(f⊗g)=(af)⊗g=f⊗(ag)
where a∈C
Differential rule
If ∂ is a differential operator
∂(f⊗g)=∂f⊗g=f⊗∂g