Skip to main content

Subsection 6.5.4 Convolution Theorem

Convolution theorem is a mathematical operation widely used in communication physics. It is a very fine method to find the inverse of Laplace Transform. If \(\mathscr{L}\{f_{1}(t)\}=F_{1}(s)\) and \(\mathscr{L}\{f_{2}(t)\}=F_{2}(s)\) then, the convolution theorem can be stated as
\begin{equation*} \mathscr{L}\{\int\limits_{0}^{t}f_{1}(x)f_{2}(t-x)\,dx\} = F_{1}(s)\cdot F_{2}(s). \end{equation*}

Proof.

Let
\begin{equation*} \int\limits_{0}^{t}f_{1}(x)f_{2}(t-x)\,dx = H(t). \end{equation*}
Then
\begin{equation*} \mathscr{L}\{H(t)\}=\int\limits_{0}^{\infty}e^{-st}H(t)\,dt \end{equation*}
\begin{equation*} = \int\limits_{t=0}^{\infty}e^{-st}\left[\int\limits_{x=0}^{t}f_{1}(x)f_{2}(t-x)\,dx\right]\,dt \end{equation*}
\begin{equation*} =\int\limits_{t=0}^{\infty}\left[\int\limits_{x=0}^{t}e^{-st}f_{2}(t-x)\,dx\right]\,dt \end{equation*}
the integration is being done w.r.t. ’x’ first and then w.r.t ’t’.
Figure 6.5.3.
The above integration is taken within the region lying below the line OP whose equation is \(x=t\) and above OT, \(t\) is taken along OT and \(x\) is along OX, with O as the origin, the axes is perpendicular to each other, as shown in figure Figure 6.5.3. If the order of integration is changed, the strip will be taken parallel to OT so that the limits of \(t\) are from \(x\) to \(\infty\) and the limits of \(x\) are from 0 to \(\infty\text{.}\) Putting \(t-x=\theta\text{,}\) we have \(\,dt=\,d\theta\text{.}\)
\begin{equation*} \therefore \quad \mathscr{L}\{H(t)\}=\int\limits_{0}^{\infty}e^{-sx}f_{1}(x)\int\limits_{0}^{\infty}e^{-s\theta}f_{2}(\theta)\,d\theta\,dx \end{equation*}
\begin{equation*} = \int\limits_{0}^{\infty}e^{-sx}f_{1}(x)F_{2}(s)\,dx = F_{1}(s)\cdot F_{2}(s) \end{equation*}
putting the value of \(H(t)\text{,}\) we get -
\begin{equation*} \mathscr{L}\{\int\limits_{0}^{t}f_{1}(x)F_{2}(t-x)\,dx\} = F_{1}(s)\cdot F_{2}(s). \end{equation*}

Subsubsection 6.5.4.1 Differential Equation with Variable Coefficients