信号公式汇总之拉普拉斯变换

拉普拉斯变换:

正变换: F ( s ) = 0 f ( t ) e s t d t = L [ f ( t ) ] F(s)=\int_0^{\infty}f(t)e^{-st}dt=\mathscr{L}[f(t)]
逆变换: F ( t ) = 1 2 π j σ j σ + j F ( s ) e s t d s = L 1 [ f ( s ) ] F(t)=\frac{ 1 }{2\pi j }\int_{\sigma- j\infty}^{\sigma+j\infty}F(s)e^{s t}ds=\mathscr{L}^{-1}[f(s)]
其中:s= σ + j ω {\sigma+j \omega}
常见信号的拉氏变换:
阶跃函数: ε ( t ) \varepsilon(t) \leftarrow \rightarrow 1 s \frac{ 1 }{s } Re{s}>0
单边指数信号: e α t ε ( t ) e^{-\alpha t}\varepsilon(t) \leftarrow \rightarrow 1 s + α \frac{ 1 }{s+\alpha } Re{s}> α -\alpha
单边正弦信号: sin ω t ε ( t ) \sin \omega t\varepsilon(t) \leftarrow \rightarrow ω s 2 + ω 2 \frac{ \omega }{s^2+\omega ^2} Re{s}>0
单边余弦信号: cos ω t ε ( t ) \cos \omega t \varepsilon(t) \leftarrow \rightarrow s s 2 + ω 2 \frac{ s }{s^2+\omega ^2} Re{s}>0
单边衰减正弦: e α t sin ω t ε ( t ) e^{-\alpha t} \sin\omega t \varepsilon(t) \leftarrow \rightarrow ω ( s + α ) 2 + ω 2 \frac{ \omega }{(s+\alpha)^2+\omega^2 } Re{s}>- α \alpha

t的正幂信号: t n ε ( t ) t^n \varepsilon(t) \leftarrow \rightarrow n ! s n + 1 \frac{n! }{s^{n+1} } Re{s}>0
L [ ε ( t ) ] = 1 s \mathscr{L}[\varepsilon(t)]=\frac{1}{s}
L [ t ] = 1 s 2 \mathscr{L}[t]=\frac{1}{s^2}
L [ t n ] = 0 t n e s t d t = n s . . . L [ t n 1 ] = . . . = n ! s n + 1 \mathscr{L}[t^n]=\int_{0_-}^{\infty} t^n e^{-st} dt=\frac{n }{s}...\mathscr{L}[t^{n-1}]=...=\frac{n! }{s^{n+1} }
冲激信号: δ ( t ) \delta (t) \leftarrow \rightarrow 1 1 Re{s}:(- \infty ,+ \infty
δ ( t t 0 ) \delta (t-t_0) \leftarrow \rightarrow e s t 0 e^{-s t_0}
δ ( t ) \delta{\prime}(t) \leftarrow \rightarrow s

拉氏变换性质:
线性: a 1 f 1 ( t ) + a 2 f 2 ( t ) a_1 f_1(t)+a_2 f_2(t) \leftarrow \rightarrow a 1 F 1 ( s ) + a 2 F 2 ( s ) a_1 F_1(s)+a_2 F_2(s)
时域微分: d f ( t ) d t \frac{d f(t)}{dt } \leftarrow \rightarrow s F ( s ) f ( 0 ) s F(s)-f(0_-)
d 2 f ( t ) d t \frac{d^2 f(t)}{dt } \leftarrow \rightarrow s 2 F ( s ) s f ( 0 ) f ( 0 ) s^2 F(s)-sf(0_-)-f{\prime}(0_-)
d n f ( t ) d t \frac{d^n f(t)}{dt } \leftarrow \rightarrow s n F ( s ) s^n F(s) - s n 1 f ( 0 ) s^{n-1}f(0_-) -…- f ( n 1 ) ( 0 ) f^{(n-1)}(0_-)
时域积分: t f ( τ ) d τ \int_{-\infty}^{t}f(\tau)d\tau \leftarrow \rightarrow F ( s ) s \frac{F(s)}{s } + f ( 1 ) ( 0 ) s \frac{f^{(-1)}(0_-)}{s }
有始函数: d f ( t ) ε ( t ) d t \frac{df(t)\varepsilon(t)}{dt } \leftarrow \rightarrow S F ( s ) SF(s)
0 t f ( τ ) d τ \int_{0_-}^{t}f(\tau)d\tau \leftarrow \rightarrow F ( s ) s \frac{F(s)}{s }
= > t ε ( t ) = 0 t ε ( τ ) d τ =>t \varepsilon(t)=\int_{0_-}^{t}\varepsilon(\tau)d\tau \leftarrow \rightarrow 1 s 2 \frac{1}{s^2 }
延时特性(时域平移): f ( t t 0 ) ε ( t t 0 ) f(t-t_0)\varepsilon(t-t_0) \leftarrow \rightarrow e s t 0 F ( s ) , t 0 > 0 e^{-s t_0}F(s),t_0>0
S域平移: f ( t ) e s t 0 f(t)e^{-s t_0} \leftarrow \rightarrow F ( s + s 0 ) F(s+s_0)
尺度变换: f ( a t ) f(at) \leftarrow \rightarrow 1 a F ( s a ) , ( a > 0 ) \frac{1}{a }F(\frac{s}{a }),(a>0)
初值定理: f ( 0 + ) = lim t 0 + f ( t ) = lim s s F ( s ) F ( s ) f(0^+)=\lim_{t\rightarrow0_+}f(t)=\lim_{s\rightarrow\infty}sF(s) (当F(s)是真分式时成立)
终值定理: f ( ) = lim t f ( t ) = lim s 0 s F ( s ) F ( s ) f(\infty)=\lim_{t\rightarrow\infty}f(t)=\lim_{s\rightarrow 0}sF(s) (F(s)极点在复频域左半平面)
卷积定理:时域 f 1 ( t ) f 2 ( t ) f_1(t)*f_2(t) \leftarrow \rightarrow F 1 ( s ) . F 2 ( s ) F_1(s).F_2(s)
卷积定理:频域 f 1 ( t ) . f 2 ( t ) f_1(t).f_2(t) \leftarrow \rightarrow 1 2 π j F 1 ( s ) F 2 ( s ) \frac{1}{2\pi j }F_1(s)*F_2(s)
复频域微分: t f ( t ) -tf(t) \leftarrow \rightarrow d ( F ( s ) d s \frac{d(F(s)}{ds}
复频域微分: f ( t ) t \frac{f(t)}{t} \leftarrow \rightarrow s F ( η ) d η \int_s^{\infty}F(\eta)d\eta

序列傅里叶变换(DTFT:discrete time Fourier transform)

正变换: $$
逆变换:
性质:序列位移:
性质:频域位移:
性质:线性加权: D T F T [ n x ( n ) ] = j [ d d ω X e ( j ω ) ] DTFT [nx(n)]=j[\frac{d}{d \omega}X e^{(j\omega)}]
性质:序列反褶: D T F T [ x ( n ) ] = X e ( j ω ) DTFT [x(-n)]=X e^{(-j\omega)}

猜你喜欢

转载自blog.csdn.net/qq_41262681/article/details/88981177