Difference between revisions of "Aufgaben:Exercise 4.10Z: Correlation Duration"
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− | {{quiz-Header|Buchseite= | + | {{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Auto-Correlation_Function |
}} | }} | ||
− | [[File:P_ID393__Sto_Z_4_10.png|right|]] | + | [[File:P_ID393__Sto_Z_4_10.png|right|frame|Pattern signals of ergodic processes]] |
− | : | + | The graphic shows pattern signals of two random processes {xi(t)} and {yi(t)} with equal power |
+ | :$$P_x = P_y = 5\hspace{0.05 cm} \rm mW.$$ | ||
+ | Assuming here the resistance $R = 50\hspace{0.05 cm}\rm \Omega$. | ||
− | |||
− | + | The random process {xi(t)} | |
− | + | * is zero mean (mx=0), | |
+ | * has the Gaussian ACF $\varphi_x (\tau) = \varphi_x (\tau = 0) \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2},$ and | ||
+ | * exhibits the equivalent ACF duration $\nabla \tau_x = 5\hspace{0.05 cm}\rm µ s $ . | ||
− | |||
− | : | + | As can be seen from the diagram below, the random process {yi(t)} has much stronger internal statistical bindings than the random process {xi(t)}. |
+ | Or, to put it another way: | ||
+ | *The random process {yi(t)} is lower frequency than $\{x_i(t)\}$. | ||
+ | *The equivalent ACF duration is $\nabla \tau_y = 10 \hspace{0.05 cm}\rm µ s $. | ||
− | |||
− | |||
− | |||
+ | From the sketch it can also be seen that {yi(t)} in contrast to {xi(t)} is not DC free. The DC signal component is rather my=−0.3V. | ||
− | === | + | |
+ | |||
+ | |||
+ | |||
+ | '''Hint''': | ||
+ | *The exercise belongs to the chapter [[Theory_of_Stochastic_Signals/Auto-Correlation_Function|Auto-Correlation Function]]. | ||
+ | *Reference is made in particular to the section [[Theory_of_Stochastic_Signals/Auto-Correlation_Function#Interpretation_of_the_auto-correlation_function|Interpretation of the auto-correlation function]]. | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ===Questions=== | ||
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {What is the standard deviation (σx) of the pattern signals of the process $\{x_i(t)\}$? |
|type="{}"} | |type="{}"} | ||
− | σx | + | $\sigma_x \ = \ $ { 0.5 3% } $\ \rm V$ |
− | { | + | {What ACF values result for $\tau = 2\hspace{0.05 cm}\rm µs$ resp. $\tau = 5\hspace{0.05 cm}\rm µ s$? |
|type="{}"} | |type="{}"} | ||
− | $\ | + | $\varphi_x(\tau = 2\hspace{0.05 cm}{\rm µ s}) \ = \ { 3.025 3% }\ \rm mW$ |
− | $\ | + | $\varphi_x(\tau = 5\hspace{0.05 cm}{\rm µ s}) \ = \ { 0.216 3% }\ \rm mW$ |
− | { | + | {What is the correlation time $T_{\rm K}$, i.e. the time at which the ACF has dropped to half of the maximum? |
|type="{}"} | |type="{}"} | ||
− | $ | + | $T_{\rm K} \ = \ { 2.35 3% }\ \rm µ s$ |
− | { | + | {What is the standard deviation (σy) of the pattern signals of the process $\{y_i(t)\}$? |
|type="{}"} | |type="{}"} | ||
− | σy | + | $\sigma_y \ = \ $ { 0.4 3% } $\ \rm V$ |
− | { | + | {Calculate the ACF $\varphi_x(\tau)$. What is the ACF value at $\tau = 10\hspace{0.05 cm}\rm µ s$? What would be the ACF curve with positive mean $(m_y = +0.3 \hspace{0.05 cm}\rm V)$? |
|type="{}"} | |type="{}"} | ||
− | $\ | + | $\varphi_y(\tau = 10\hspace{0.05 cm}{\rm µ s}) \ = \ { 1.938 3% }\ \rm mW$ |
Line 54: | Line 69: | ||
</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | + | '''(1)''' The second moment results to $m_{2x} = R \cdot P_x = 50 \hspace{0.05 cm}{\rm \Omega}\cdot 5 \hspace{0.05 cm}{\rm mW}= 0.25 \hspace{0.05 cm}{\rm V}^2.$ | |
+ | *From this follows the standard deviation $\sigma_x\hspace{0.15 cm}\underline{= 0.5\hspace{0.05 cm}{\rm V}}$. | ||
− | + | ||
+ | |||
+ | '''(2)''' Because of $P_x = \varphi_x (\tau = 0)$ holds for the ACF in general: | ||
:φx(τ)=5mW⋅e−π⋅(τ/∇τx)2. | :φx(τ)=5mW⋅e−π⋅(τ/∇τx)2. | ||
+ | *From this we obtain: | ||
+ | :\varphi_x (\tau = {\rm 2\hspace{0.1cm} µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- {\rm 0.16 }\pi } \hspace{0.15cm}\underline{= 3.025 \hspace{0.1cm} \rm mW}, | ||
+ | :\varphi_x (\tau = {\rm 5\hspace{0.1cm} \rm µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi } \hspace{0.15cm}\underline{= 0.216 \hspace{0.1cm} \rm mW}. | ||
+ | |||
+ | |||
+ | |||
+ | [[File:P_ID394__Sto_Z_4_10_e.png|right|frame|Two times Gaussian ACF]] | ||
+ | '''(3)''' Here the following determination equation holds: | ||
+ | :e−π⋅(TK/∇τx)2!=0.5⇒(TK/∇τx)2=√ln(2)/π. | ||
+ | |||
+ | *From this follows T_{\rm K}\hspace{0.15 cm}\underline{= 2.35\hspace{0.05 cm}{\rm µ s}}. | ||
+ | *With another ACF form, a different ratio is obtained for TK/∇τx. | ||
+ | |||
− | |||
− | |||
− | |||
− | |||
− | |||
− | + | '''(4)''' Because of $P_x = P_y the second order moments of x and y are equal 0.25\hspace{0.05 cm}\rm V^2$. | |
+ | *Taking into account the mean value $m_y = -0.3 \hspace{0.05 cm}\rm V$ holds: | ||
+ | :m2y+σ2y=0.25V2. | ||
+ | *From this follows: | ||
+ | :$$\sigma_y\hspace{0.15 cm}\underline{= 0.4\hspace{0.05 cm}{\rm V}}.$$ | ||
− | |||
− | |||
− | |||
− | |||
− | + | '''(5)''' In terms of unit resistance $ R = 1 \hspace{0.05 cm}{\rm \Omega}$ the ACF of the process $\{y_i(t)\}$ is: | |
:φy(τ)=m2y+σ2y⋅e−π⋅(τ/∇τy)2. | :φy(τ)=m2y+σ2y⋅e−π⋅(τ/∇τy)2. | ||
− | + | *On the right you can see the ACF curve. Related to the resistor $ R = 50 \hspace{0.05 cm}{\rm \Omega}$ results in the following ACF values: | |
− | :$$\varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0. | + | :$$\varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0.5cm} \varphi_y (\tau \rightarrow \infty) = 1.8\hspace{0.1cm} {\rm mW} .$$ |
− | + | *From this follows: | |
− | :φy(τ)=1.8mW+3.2mW⋅e−π⋅(τ/∇τy)2 | + | :$$\varphi_y(\tau) = 1.8 \hspace{0.1cm} {\rm mW} + 3.2 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2} \hspace{0.3cm }\Rightarrow \hspace{0.3cm }\varphi_y(\tau = 10\hspace{0.05 cm}{\rm µ s}) |
+ | \hspace{0.15 cm}\underline{=1.938\hspace{0.05 cm}\rm mW}.$$ | ||
− | + | *With positive mean my $(havingthesamemagnitude)$, there would be no change in the ACF, since my is squared in the ACF equation. | |
{{ML-Fuß}} | {{ML-Fuß}} | ||
− | [[Category: | + | [[Category:Theory of Stochastic Signals: Exercises|^4.4 Auto-Correlation Function^]] |
Latest revision as of 19:52, 20 March 2022
The graphic shows pattern signals of two random processes {xi(t)} and {yi(t)} with equal power
- Px=Py=5mW.
Assuming here the resistance R=50Ω.
The random process {xi(t)}
- is zero mean (mx=0),
- has the Gaussian ACF φx(τ)=φx(τ=0)⋅e−π⋅(τ/∇τx)2, and
- exhibits the equivalent ACF duration \nabla \tau_x = 5\hspace{0.05 cm}\rm µ s .
As can be seen from the diagram below, the random process \{y_i(t)\} has much stronger internal statistical bindings than the random process \{x_i(t)\}.
Or, to put it another way:
- The random process \{y_i(t)\} is lower frequency than \{x_i(t)\}.
- The equivalent ACF duration is \nabla \tau_y = 10 \hspace{0.05 cm}\rm µ s .
From the sketch it can also be seen that \{y_i(t)\} in contrast to \{x_i(t)\} is not DC free. The DC signal component is rather m_y = -0.3 \hspace{0.05 cm}\rm V.
Hint:
- The exercise belongs to the chapter Auto-Correlation Function.
- Reference is made in particular to the section Interpretation of the auto-correlation function.
Questions
Solution
- From this follows the standard deviation \sigma_x\hspace{0.15 cm}\underline{= 0.5\hspace{0.05 cm}{\rm V}}.
(2) Because of P_x = \varphi_x (\tau = 0) holds for the ACF in general:
- \varphi_x (\tau) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_x)^2}.
- From this we obtain:
- \varphi_x (\tau = {\rm 2\hspace{0.1cm} µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- {\rm 0.16 }\pi } \hspace{0.15cm}\underline{= 3.025 \hspace{0.1cm} \rm mW},
- \varphi_x (\tau = {\rm 5\hspace{0.1cm} \rm µ s}) = 5 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi } \hspace{0.15cm}\underline{= 0.216 \hspace{0.1cm} \rm mW}.
(3) Here the following determination equation holds:
- {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(T_{\rm K} / {\rm \nabla} \tau_x)^2} \stackrel{!}{=} {\rm 0.5} \hspace{0.5cm}\Rightarrow\hspace{0.5cm} (T_{\rm K} / {\rm \nabla} \tau_x)^2 = \sqrt{{ \ln(2)}/{\pi}}\hspace{0.05cm}.
- From this follows T_{\rm K}\hspace{0.15 cm}\underline{= 2.35\hspace{0.05 cm}{\rm µ s}}.
- With another ACF form, a different ratio is obtained for T_{\rm K} / {\rm \nabla} \tau_x.
(4) Because of P_x = P_y the second order moments of x and y are equal 0.25\hspace{0.05 cm}\rm V^2.
- Taking into account the mean value m_y = -0.3 \hspace{0.05 cm}\rm V holds:
- m_y^2 + \sigma_y^2 = \rm 0.25 \hspace{0.05 cm} V^2.
- From this follows:
- \sigma_y\hspace{0.15 cm}\underline{= 0.4\hspace{0.05 cm}{\rm V}}.
(5) In terms of unit resistance R = 1 \hspace{0.05 cm}{\rm \Omega} the ACF of the process \{y_i(t)\} is:
- \varphi_y (\tau) = m_y^2 + \sigma_y^2 \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2}.
- On the right you can see the ACF curve. Related to the resistor R = 50 \hspace{0.05 cm}{\rm \Omega} results in the following ACF values:
- \varphi_y (\tau = 0) = 5 \hspace{0.1cm} {\rm mW} , \hspace{0.5cm} \varphi_y (\tau \rightarrow \infty) = 1.8\hspace{0.1cm} {\rm mW} .
- From this follows:
- \varphi_y(\tau) = 1.8 \hspace{0.1cm} {\rm mW} + 3.2 \hspace{0.1cm} {\rm mW} \cdot {\rm e}^{- \pi \hspace{0.03cm} \cdot \hspace{0.03cm}(\tau / {\rm \nabla} \tau_y)^2} \hspace{0.3cm }\Rightarrow \hspace{0.3cm }\varphi_y(\tau = 10\hspace{0.05 cm}{\rm µ s}) \hspace{0.15 cm}\underline{=1.938\hspace{0.05 cm}\rm mW}.
- With positive mean m_y (having the same magnitude), there would be no change in the ACF, since m_y is squared in the ACF equation.