一個訊號s(t),自相關函數為
R
(
τ
)
=
∫
−
∞
∞
s
(
t
)
s
∗
(
t
−
τ
)
d
t
{\displaystyle R(\tau )=\int _{-\infty }^{\infty }s(t)s^{*}(t-\tau )\,dt}
如果
R
(
τ
)
{\displaystyle R(\tau )}
為時間相依性(time-dependent),則時間相依自相關(time-dependent auto-correlation)為
R
(
t
,
τ
)
{\displaystyle R(t,\tau )}
,
時間相依(時變)頻譜(time-dependent spectrum)可以表示的形式類似於傳統的功率譜,即對時間相依自相關函數做傅立葉變換。
P
(
t
,
ω
)
=
∫
−
∞
∞
R
(
t
,
τ
)
e
−
j
ω
τ
d
τ
{\displaystyle P(t,\omega )=\int _{-\infty }^{\infty }R(t,\tau )e^{-j\omega \tau }\,d\tau }
不同的時間相依自相關會導致不同的時間相依功率譜。
如果
R
(
t
,
τ
)
=
s
(
t
+
τ
2
)
s
∗
(
t
−
τ
2
)
{\displaystyle R(t,\tau )=s\left(t+{\frac {\tau }{2}}\right)s^{*}\left(t-{\frac {\tau }{2}}\right)}
,則時間相依功率譜變成為Wigner distribution
若對
R
(
t
,
τ
)
{\displaystyle R(t,\tau )}
中的t做傅立葉逆轉換,得到另一個時頻表示,對稱模糊函數(symmetric ambiguity function,SAF)
S
A
F
s
(
θ
,
τ
)
=
1
2
π
∫
−
∞
∞
s
(
t
+
τ
2
)
s
∗
(
t
−
τ
2
)
e
j
θ
t
d
t
{\displaystyle SAF_{s}(\theta ,\tau )={\frac {1}{2\pi }}\int _{-\infty }^{\infty }s\left(t+{\frac {\tau }{2}}\right)s^{*}\left(t-{\frac {\tau }{2}}\right)e^{j\theta t}\,dt}
模糊函數反映信號在時間和相位的相關性,並已廣泛應用在雷達和聲納系統上。
給一個對稱模糊函數
S
A
F
s
(
θ
,
τ
)
{\displaystyle SAF_{s}(\theta ,\tau )}
,透過傅立葉變換可以得到時間相依自相關:
∫
−
∞
∞
S
A
F
s
(
θ
,
τ
)
e
−
j
θ
t
d
θ
=
s
(
t
+
τ
2
)
s
∗
(
t
−
τ
2
)
{\displaystyle \int _{-\infty }^{\infty }SAF_{s}(\theta ,\tau )e^{-j\theta t}\,d\theta =s\left(t+{\frac {\tau }{2}}\right)s^{*}\left(t-{\frac {\tau }{2}}\right)}
由上式可以推得
W
D
s
(
t
,
ω
)
=
∫
−
∞
∞
∫
−
∞
∞
S
A
F
s
(
θ
,
τ
)
e
−
j
(
ω
τ
+
θ
t
)
d
θ
d
τ
{\displaystyle WD_{s}(t,\omega )=\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }SAF_{s}(\theta ,\tau )e^{-j(\omega \tau +\theta t)}\,d\theta \,d\tau }
也就是對對稱模糊函數 做兩次傅立葉變換可以得到Wigner distribution
一個訊號為兩個Gaussian函數的和:
s
(
t
)
=
∑
i
=
1
2
s
i
(
t
)
=
∑
i
=
1
2
α
π
4
e
−
α
2
(
t
−
t
i
)
2
+
j
ω
i
t
{\displaystyle s(t)=\sum _{i=1}^{2}s_{i}(t)=\sum _{i=1}^{2}{\sqrt[{4}]{\frac {\alpha }{\pi }}}e^{-{\tfrac {\alpha }{2}}(t-t_{i})^{2}+j\omega _{i}t}}
⇒
S
A
F
s
(
θ
,
τ
)
=
∑
i
=
1
2
S
A
F
s
i
(
θ
,
τ
)
+
S
A
F
s
1
,
s
2
(
θ
,
τ
)
+
S
A
F
s
2
,
s
1
(
θ
,
τ
)
{\displaystyle \Rightarrow SAF_{s}(\theta ,\tau )=\sum _{i=1}^{2}SAF_{si}(\theta ,\tau )+SAF_{s1,s2}(\theta ,\tau )+SAF_{s2,s1}(\theta ,\tau )}
其中
S
A
F
s
1
(
θ
,
τ
)
,
S
A
F
s
2
(
θ
,
τ
)
{\displaystyle SAF_{s1}(\theta ,\tau ),SAF_{s2}(\theta ,\tau )}
集中在原點(0,0),而
S
A
F
s
1
,
s
2
(
θ
,
τ
)
{\displaystyle SAF_{s1,s2}(\theta ,\tau )}
集中在
(
t
1
−
t
2
,
ω
1
−
ω
2
)
{\displaystyle (t_{1}-t_{2},\omega _{1}-\omega _{2})}
,而
S
A
F
s
2
,
s
1
(
θ
,
τ
)
{\displaystyle SAF_{s2,s1}(\theta ,\tau )}
相似於
S
A
F
s
1
,
s
2
(
θ
,
τ
)
{\displaystyle SAF_{s1,s2}(\theta ,\tau )}
,除了中心點在
(
t
2
−
t
1
,
ω
2
−
ω
1
)
{\displaystyle (t_{2}-t_{1},\omega _{2}-\omega _{1})}
S
A
F
s
1
,
s
2
(
θ
,
τ
)
=
e
−
1
4
α
(
θ
−
ω
d
)
2
+
α
4
(
τ
−
t
d
)
2
e
j
(
ω
u
τ
−
θ
t
u
+
ω
d
t
u
)
{\displaystyle SAF_{s1,s2}(\theta ,\tau )=e^{-{\tfrac {1}{4\alpha }}(\theta -\omega _{d})^{2}+{\tfrac {\alpha }{4}}(\tau -t_{d})^{2}}e^{j(\omega _{u}\tau -\theta t_{u}+\omega _{d}t_{u})}}
,
t
u
=
t
1
+
t
2
2
{\displaystyle t_{u}={\tfrac {t_{1}+t_{2}}{2}}}
,
ω
u
=
ω
1
+
ω
2
2
{\displaystyle \omega _{u}={\tfrac {\omega _{1}+\omega _{2}}{2}}}
,
t
d
=
t
1
−
t
2
{\displaystyle t_{d}=t_{1}-t_{2}}
,
ω
d
=
ω
1
−
ω
2
{\displaystyle \omega _{d}=\omega _{1}-\omega _{2}}
模糊域(ambiguity domain)的auto-term與cross-term
編輯
從範例中得知一項重要事實,即為,在模糊域(ambiguity domain)中的auto-term總是集中在原點(0,0),而cross-term總是在遠離原點處,所以可以用一個2D lowpass filter在模糊域中抑制cross-term的干擾,如下:
∫
−
∞
∞
∫
−
∞
∞
S
A
F
s
(
θ
,
τ
)
Φ
(
θ
,
τ
)
e
−
j
(
θ
t
+
ω
τ
)
d
θ
d
τ
{\displaystyle \int _{-\infty }^{\infty }\int _{-\infty }^{\infty }SAF_{s}(\theta ,\tau )\Phi (\theta ,\tau )e^{-j(\theta t+\omega \tau )}\,d\theta \,d\tau }
,其中
Φ
(
θ
,
τ
)
{\displaystyle \Phi (\theta ,\tau )}
為2D lowpass filter
如果
∫
−
∞
∞
∫
−
∞
∞
Φ
(
θ
,
τ
)
d
θ
d
τ
=
ϕ
(
t
,
ω
)
{\displaystyle \int _{-\infty }^{\infty }\int _{-\infty }^{\infty }\Phi (\theta ,\tau )\,d\theta \,d\tau =\phi (t,\omega )}
,則
∫
−
∞
∞
∫
−
∞
∞
S
A
F
s
(
θ
,
τ
)
Φ
(
θ
,
τ
)
e
−
j
(
θ
t
+
ω
τ
)
d
θ
d
τ
{\displaystyle \int _{-\infty }^{\infty }\int _{-\infty }^{\infty }SAF_{s}(\theta ,\tau )\Phi (\theta ,\tau )e^{-j(\theta t+\omega \tau )}\,d\theta \,d\tau }
=
∫
−
∞
∞
∫
−
∞
∞
ϕ
(
x
,
y
)
W
D
s
(
t
−
x
,
ω
−
y
)
d
x
d
y
=
S
W
D
(
t
,
ω
)
{\displaystyle =\int _{-\infty }^{\infty }\int _{-\infty }^{\infty }\phi (x,y)WD_{s}(t-x,\omega -y)\,dx\,dy=SWD(t,\omega )}
其中SWD為smoothed Wigner distribution
通常
Φ
(
θ
,
τ
)
{\displaystyle \Phi (\theta ,\tau )}
( 和
ϕ
(
t
,
ω
)
{\displaystyle \phi (t,\omega )}
)當作kernal function,用來控制SWD的特性。
若Wigner分佈和對稱模糊函數用大小(magnitude)及相位(phase)表示,如下:
W
D
s
1
,
s
2
(
t
,
ω
)
=
A
W
D
(
t
,
ω
)
e
j
φ
W
D
(
t
,
ω
)
{\displaystyle WD_{s1,s2}(t,\omega )=A_{WD}(t,\omega )e^{j\varphi _{WD}(t,\omega )}}
S
A
F
s
1
,
s
2
(
θ
,
τ
)
=
A
S
A
F
(
θ
,
τ
)
e
j
φ
S
A
F
(
θ
,
τ
)
{\displaystyle SAF_{s1,s2}(\theta ,\tau )=A_{SAF}(\theta ,\tau )e^{j\varphi _{SAF}(\theta ,\tau )}}
而
∂
∂
θ
φ
S
A
F
(
θ
,
τ
)
=
−
t
u
{\displaystyle {\frac {\partial }{\partial \theta }}\varphi _{SAF}(\theta ,\tau )=-t_{u}}
,
∂
∂
τ
φ
S
A
F
(
θ
,
τ
)
=
ω
u
{\displaystyle {\frac {\partial }{\partial \tau }}\varphi _{SAF}(\theta ,\tau )=\omega _{u}}
也就是說對對稱模糊函數 的相位做偏微分,會等於Wigner分佈的時頻(time-frequency)中心。
相反地,
∂
∂
ω
φ
W
D
(
t
,
ω
)
=
t
d
{\displaystyle {\frac {\partial }{\partial \omega }}\varphi _{WD}(t,\omega )=t_{d}}
,
∂
∂
t
φ
W
D
(
t
,
ω
)
=
ω
d
{\displaystyle {\frac {\partial }{\partial t}}\varphi _{WD}(t,\omega )=\omega _{d}}
則為對Wigner分佈的相位做偏微分,會等於對稱模糊函數的中心。
如果
ω
1
=
ω
2
=
ω
0
{\displaystyle \omega _{1}=\omega _{2}=\omega _{0}}
,則
S
A
F
s
1
,
s
2
(
θ
,
τ
)
=
e
−
[
1
4
α
θ
2
+
α
4
(
τ
−
t
d
)
2
]
e
j
(
ω
0
τ
−
θ
t
u
)
{\displaystyle SAF_{s1,s2}(\theta ,\tau )=e^{-[{\tfrac {1}{4\alpha }}\theta ^{2}+{\tfrac {\alpha }{4}}(\tau -t_{d})^{2}]}e^{j(\omega _{0}\tau -\theta t_{u})}}
會集中在
τ
{\displaystyle \tau }
軸上。
如果
t
1
=
t
2
=
t
0
{\displaystyle t_{1}=t_{2}=t_{0}}
,則
S
A
F
s
1
,
s
2
(
θ
,
τ
)
=
e
−
[
1
4
α
(
θ
−
ω
d
)
2
+
α
4
τ
2
]
e
j
[
ω
u
τ
−
(
θ
−
ω
d
)
t
0
]
{\displaystyle SAF_{s1,s2}(\theta ,\tau )=e^{-[{\tfrac {1}{4\alpha }}(\theta -\omega _{d})^{2}+{\tfrac {\alpha }{4}}\tau ^{2}]}e^{j[\omega _{u}\tau -(\theta -\omega _{d})t_{0}]}}
會集中在
θ
{\displaystyle \theta }
軸上。