Fourier Transform
Table of Contents
Fourier Series
Continuous Fourier Series
Discrete Fourier Series
Properties
where subscript \(r\), \(i\), \(e\), and \(o\) mean the real, imagnary, even, and odd parts of an expression, respectively, i.e,
\begin{align*} \tilde{x}(n) &= \tilde{x}_e(n) + \tilde{x}_o(n) \\ &= \tilde{x}_r(n) + j \tilde{x}_i(n) \\ \tilde{x}_e(n) &= \frac{\tilde{x}(n) + \tilde{x}(-n)}{2} \\ \tilde{x}_o(n) &= \frac{\tilde{x}(n) - \tilde{x}(-n)}{2} \\ \tilde{X}(k) &= \tilde{X}_e(k) + \tilde{X}_o(k) \\ &= \tilde{X}_r(k) + j \tilde{X}_i(k) \\ \tilde{X}_e(k) &= \frac{\tilde{X}(k) + \tilde{X}(-k)}{2} \\ \tilde{X}_o(k) &= \frac{\tilde{X}(k) - \tilde{X}(-k)}{2}. \end{align*}Wide Sense Fourier Series
Continuous
Given a continuous time signal space \(\mathcal{S}\) defined in range \((t_1, t_2)\), if \(\{\phi_i(t) \mid i=1, 2, \ldots, N\}\) is a complete orthogonal basis in \(\mathcal{S}\), \(\forall x(t) \in \mathcal{S}\), it can be represented as a linear combination of the basis, i.e.,
\begin{align*} x(t) = \sum_{i=1}^N a_i \phi_i(t), \end{align*}where
\begin{align*} a_i = \dfrac{\int_{t_1}^{t_2} x(t) \phi_i^{*}(t) dt}{\int_{t_1}^{t_2}|\phi_i(t)|^2dt}, \quad i = 1, 2, \ldots, N. \end{align*}Discrete
Given a discrete time signal space \(\mathcal{S}\) defined in range \((n_1, n_2)\), if \(\{\phi_i[n] \mid i=1, 2, \ldots, N\}\) is a complete orthogonal basis in \(\mathcal{S}\), \(\forall x[n] \in \mathcal{S}\), it can be represented as a linear combination of the basis, i.e.,
\begin{align*} x[n] = \sum_{i=1}^N a_i \phi_i[n], \end{align*}where
\begin{align*} a_i = \dfrac{\sum_{n=n_1}^{n_2} x[n] \phi_i^{*}[n]}{\sum_{n=n_1}^{n_2}|\phi_i[n]|^2}, \quad i = 1, 2, \ldots, N. \end{align*}Continuous Fourier Transform
Definition
Properties
Parseval Theorem
Fourier Transform Pairs
Discrete Time Fourier Transform
Definition
Properties
where subscript \(r\), \(i\), \(e\), and \(o\) mean the real, imagnary, even, and odd parts of an expression, respectively, i.e,
\begin{align*} x(n) &= x_e(n) + x_o(n) \\ &= x_r(n) + j x_i(n) \\ x_e(n) &= \frac{x(n) + x^{*}(-n)}{2} \\ x_o(n) &= \frac{x(n) - x^{*}(-n)}{2} \\ X(e^{j\omega}) &= X_e(e^{j\omega}) + X_o(e^{j\omega}) \\ &= X_r(e^{j\omega}) + j X_i(e^{j\omega}) \\ X_e(e^{j\omega}) &= \frac{X(e^{j\omega}) + X^{*}(e^{-j\omega})}{2} \\ X_o(e^{j\omega}) &= \frac{X(e^{j\omega}) - X^{*}(e^{-j\omega})}{2}. \end{align*}Parseval Theorem
Discrete Fourier Transform
Definition
Properties
where subscript \(r\), \(i\), \(e\), and \(o\) mean the real, imagnary, conjugate symmetric, and conjugate antisymmetric parts of an expression, respectively, i.e,
\begin{align*} x(n) &= x_e(n) + x_o(n) \\ &= x_r(n) + j x_i(n) \\ x_e(n) &= \frac{x(n) + x^{*}(N-n)}{2} \\ x_o(n) &= \frac{x(n) - x^{*}(N-n)}{2} \\ X(k) &= X_e(k) + X_o(k) \\ &= X_r(k) + j X_i(k) \\ X_e(k) &= \frac{X(k) + X^{*}(N-k)}{2} \\ X_o(k) &= \frac{X(k) - X^{*}(N-k)}{2}. \end{align*}Summary
- \(X(k) = X(e^{j\omega}) \mid_{\omega=\frac{2\pi}{N}k} = X(z)\mid_{z=e^{j\frac{2\pi}{N}k}}\), \(k = 0, \ldots, N-1\).
- Correspondences
- Time domain \(\leftrightarrow\) frequency domain
- Frequency domain \(\leftrightarrow\) time domain
- Real \(\leftrightarrow\) conjugate symmetric
- Pure imagnary \(\leftrightarrow\) conjugate antisymmetric
- Discrete \(\leftrightarrow\) periodic
- Continuous \(\leftrightarrow\) aperiodic