Z transform
Sampling
sampling interval T
Laplace Transform
LT
考虑$\frac{1}{2\pi} \int_{-\infty}^{+\infty} e^{-jkT\omega} e^{j\omega t} d\omega$, 引入参量a
To sum up, 只需合理选择$\beta$, 保证$F_T(s) = \sum_{k=0}^{\infty} f(kT) [e^{-Ts}]^k$收敛即可
考虑信号保持
$\hat{f}_T(t)=f(kT)$保持$t \in [kT, (k+1)T)$
则有
一方面,减少间隔T
考虑Fourier与Z
反向思考, Z 和 Fourier的反变换:
Here replace $z = e^{sT}$, we have
如果思考有函数
If we know $f_T(t) = \mathbf{L^{-1}}[F_T(s)], f_u(t) = \mathbf{L^{-1}}[F_u(s)]$\
Then we have $f_z(t) = f_T(t) * f_u(t)$
Reason is
we will find poles for $F_z(s)$, here
In other way
Relationship to Z
define $x(n), X(z)$, with $e^{sT} = z, \text{Im}(s)\in (-\frac{\pi}{T}, +\frac{\pi}{T}), z\in C$, here we have
So inverse Z transformation
Relationship to 离散时间傅里叶变换DTFT
when $n \in (\infty, +\infty), \beta = 0$
Here we have $e^{sT} = e^{(\beta + j\omega)T} = e^{B} e^{j\Omega}$