Difference between revisions of "Aufgaben:Exercise 5.5Z: ACF after 1st Order Filter"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Erzeugung vorgegebener AKF-Eigenschaften
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{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Creation_of_Predefined_ACF_Properties
 
}}
 
}}
  
[[File:P_ID565__Sto_Z_5_5_neu.png|right|frame|Nichtrekursives Filter mit Gleichanteil]]
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[[File:P_ID565__Sto_Z_5_5_neu.png|right|frame|Non-recursive filter <br>with DC component]]
Wir betrachten hier ein nichtrekursives Filter erster Ordnung&nbsp; $(M = 1)$.
+
We consider here a first order non-recursive filter&nbsp; $(M = 1)$.
*Die Filterkoeffizienten seien&nbsp; $a_0 = 0.4$&nbsp; und&nbsp; $a_1 = 0.3$.  
+
*Let the filter coefficients be&nbsp; $a_0 = 0.4$&nbsp; and&nbsp; $a_1 = 0.3$.  
*Am Filterausgang wird eine Konstante&nbsp; $K$&nbsp; hinzuaddiert, die bis einschließlich Teilaufgabe&nbsp; '''(3)'''&nbsp; zu Null gesetzt werden soll.
+
*A constant&nbsp; $K$&nbsp; is added at the filter output, which is to be set to zero up to and including subtask&nbsp; '''(3)'''.&nbsp;  
  
  
Die einzelnen Elemente der Eingangsfolge&nbsp; $\left\langle \hspace{0.05cm}{x_\nu  } \hspace{0.05cm}\right\rangle$
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The individual elements of the input sequence&nbsp; $\left\langle \hspace{0.05cm}{x_\nu  } \hspace{0.05cm}\right\rangle$
* sind gaußisch sowie mittelwertfrei,&nbsp; und
+
* are Gaussian as well as mean-free,&nbsp; and
* besitzen jeweils die Streuung&nbsp; $\sigma_x = 1$.
+
* have in each case the standard deviation&nbsp; $\sigma_x = 1$.
  
  
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Notes:  
 
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*The exercise belongs to the chapter&nbsp; [[Theory_of_Stochastic_Signals/Creation_of_Predefined_ACF_Properties|Creation of Predefined ACF Properties]].
 
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*Reference is also made to the chapters &nbsp; [[Theory_of_Stochastic_Signals/Auto-Correlation_Function_(ACF)|Auto-Correlation Function]] &nbsp; and &nbsp; [[Theory_of_Stochastic_Signals/Power-Spectral_Density|Power-Spectral Density]].
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel&nbsp; [[Theory_of_Stochastic_Signals/Erzeugung_vorgegebener_AKF-Eigenschaften|Erzeugung vorgegebener AKF-Eigenschaften]].
 
*Bezug genommen wird auch auf die Kapitel&nbsp; [[Theory_of_Stochastic_Signals/Autokorrelationsfunktion_(AKF)|Autokorrelationsfunktion]]&nbsp; sowie&nbsp; [[Theory_of_Stochastic_Signals/Leistungsdichtespektrum_(LDS)|Leistungsdichtespektrum]].
 
 
   
 
   
  
  
===Fragebogen===
+
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche Aussagen sind bezüglich der Ausgangs&ndash;AKF zutreffend, wenn&nbsp; $K = 0$&nbsp; gilt?&nbsp; Begründen Sie Ihre Ergebnisse.
+
{Which statements are true regarding the output ACF when&nbsp; $K = 0$?&nbsp; &nbsp; Justify your results.
 
|type="[]"}
 
|type="[]"}
- Der AKF-Wert&nbsp; $\varphi_y(0)$&nbsp; gibt die Streuung&nbsp; $\sigma_y$&nbsp; an.
+
- The ACF value&nbsp; $\varphi_y(0)$&nbsp; indicates the standard deviation&nbsp; $\sigma_y$.
+ Alle AKF-Werte&nbsp; $\varphi_y(k \cdot T_{\rm A})$&nbsp; mit&nbsp; $k \ge 2$&nbsp; sind Null.
+
+ All ACF values&nbsp; $\varphi_y(k \cdot T_{\rm A})$&nbsp; with&nbsp; $k \ge 2$&nbsp; are zero.
+ Das Leistungsdichtespektrum (LDS)&nbsp; ${\it \Phi}_y(f)$&nbsp; verläuft cosinusförmig.
+
+ The power-spectral density&nbsp; $\rm (PSD)$&nbsp; ${\it \Phi}_y(f)$&nbsp; is cosinusoidal.
  
  
{Berechnen Sie die&nbsp; AKF&ndash;Werte&nbsp; $\varphi_y(k \cdot T_{\rm A})$&nbsp; für&nbsp; $k = 0$&nbsp; und&nbsp; $k = 1$.
+
{Calculate the ACF values&nbsp; $\varphi_y(k \cdot T_{\rm A})$&nbsp; for&nbsp; $k = 0$&nbsp; and&nbsp; $k = 1$.
 
|type="{}"}
 
|type="{}"}
 
$\varphi_y(0) \ = \ $  { 0.25 3% }
 
$\varphi_y(0) \ = \ $  { 0.25 3% }
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{Welche Werte muss man für&nbsp; $a_0$&nbsp; und&nbsp; $a_1$&nbsp; einstellen, wenn bei gleicher AKF&ndash;Form die Streuung&nbsp; $\sigma_y = 1$&nbsp; betragen soll?&nbsp; Es sei&nbsp; $a_0 > a_1$.
+
{What values do you need to set for&nbsp; $a_0$&nbsp; and&nbsp; $a_1$&nbsp; if you want the standard deviation to be&nbsp; $\sigma_y = 1$&nbsp; for the same ACF shape?&nbsp; Let&nbsp; $a_0 > a_1$.
 
|type="{}"}
 
|type="{}"}
 
$a_0 \ =  \ $ { 0.8 3% }
 
$a_0 \ =  \ $ { 0.8 3% }
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{Es gelte&nbsp; $a_0 = 0.4$&nbsp; und&nbsp; $a_1 = 0.3$.&nbsp; Wie groß ist die Konstante&nbsp; $K$&nbsp; zu wählen, damit sich&nbsp; $\varphi_y(0)= 0.5$&nbsp; ergibt?
+
{Let&nbsp; $a_0 = 0.4$&nbsp; and&nbsp; $a_1 = 0.3$.&nbsp; How large should the constant&nbsp; $K$&nbsp; be chosen so that&nbsp; $\varphi_y(0)= 0.5$?&nbsp;
 
|type="{}"}
 
|type="{}"}
 
$K \ =  \ $ { 0.5 3% }
 
$K \ =  \ $ { 0.5 3% }
  
  
{Berechnen Sie mit diesem&nbsp; $K$&ndash;Wert die AKF&ndash;Werte für&nbsp; $k = 1$&nbsp; und&nbsp; $k = 2$.
+
{Using this&nbsp; $K$ value,&nbsp; calculate the ACF values for&nbsp; $k = 1$&nbsp; and&nbsp; $k = 2$.
 
|type="{}"}
 
|type="{}"}
 
$\varphi_y(T_{\rm A}) \ =  \ $ { 0.37 3% }
 
$\varphi_y(T_{\rm A}) \ =  \ $ { 0.37 3% }
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{Welcher Wert ergibt sich nun für die Streuung&nbsp; $\sigma_y$?
+
{What is the  standard deviation&nbsp; $\sigma_y$&nbsp; now?
 
|type="{}"}
 
|type="{}"}
 
$\sigma_y \ =  \ $ { 0.5 3% }
 
$\sigma_y \ =  \ $ { 0.5 3% }
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</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Richtig sind <u>die Lösungsvorschläge 2 und 3</u>:
+
'''(1)'''&nbsp; <u>Solutions 2 and 3</u>&nbsp; are correct:
*Der AKF&ndash;Wert&nbsp; $\varphi_y(0)$&nbsp; gibt die Varianz (Leistung)&nbsp; $\sigma_y^2$&nbsp; an und nicht die Streuung (den Effektivwert)&nbsp; $\sigma_y$.  
+
*The ACF value&nbsp; $\varphi_y(0)$&nbsp; gives the variance&nbsp; ("power")&nbsp; $\sigma_y^2$&nbsp; and not the&nbsp; "standard deviation"&nbsp; $\sigma_y$.  
*Da ein nichtrekursives Filter erster Ordnung vorliegt, sind alle AKF&ndash;Werte&nbsp;  $\varphi_y(k \cdot T_{\rm A})= 0$&nbsp; für $k \ge 2$.  
+
*Since a first-order non-recursive filter is present,&nbsp; all ACF values are&nbsp;  $\varphi_y(k \cdot T_{\rm A})= 0$&nbsp; for $|k| \ge 2$.  
*Der AKF&ndash;Wert&nbsp; $\varphi_y(- T_{\rm A})$&nbsp; ist gleich&nbsp; $\varphi_y(+ T_{\rm A})$.  
+
*The ACF value&nbsp; $\varphi_y(- T_{\rm A})$&nbsp; is equal to&nbsp; $\varphi_y(+ T_{\rm A})$.  
*Diese beiden AKF-Werte führen zu einer Cosinusfunktion im Leistungsdichtespektrum, zu der sich noch der Gleichanteil&nbsp; $\varphi_y(0)$&nbsp; hinzuaddiert.
+
*These two ACF values result in a cosine function in the power-spectral density,&nbsp; to which the DC component&nbsp; $\varphi_y(0)$&nbsp; is added.
 
   
 
   
  
  
'''(2)'''&nbsp; Die allgemeine Gleichung lautet mit&nbsp; $M = 1$&nbsp; für&nbsp; $k \in \{0, \ 1\}$:
+
'''(2)'''&nbsp; The general equation with&nbsp; $M = 1$&nbsp; for&nbsp; $k \in \{0, \ 1\}$ is:
 
:$$\varphi _y ( {k  \cdot T_{\rm A} } ) = \sigma _x ^2 \cdot \sum\limits_{\mu  = 0}^{M - k} {a_\mu  \cdot a_{\mu  + k} } .$$
 
:$$\varphi _y ( {k  \cdot T_{\rm A} } ) = \sigma _x ^2 \cdot \sum\limits_{\mu  = 0}^{M - k} {a_\mu  \cdot a_{\mu  + k} } .$$
  
*Daraus erhält man mit&nbsp; $\sigma_x = 1$:
+
*From this we obtain with&nbsp; $\sigma_x = 1$:
 
:$$\varphi _y( 0 ) = a_0 ^2  + a_1 ^2  = 0.4^2  + 0.3^2 \hspace{0.15cm}\underline { = 0.25},$$
 
:$$\varphi _y( 0 ) = a_0 ^2  + a_1 ^2  = 0.4^2  + 0.3^2 \hspace{0.15cm}\underline { = 0.25},$$
 
:$$\varphi _y ( { T_{\rm A} } ) = a_0  \cdot a_1  = 0.4 \cdot 0.3 \hspace{0.15cm}\underline {= 0.12}.$$
 
:$$\varphi _y ( { T_{\rm A} } ) = a_0  \cdot a_1  = 0.4 \cdot 0.3 \hspace{0.15cm}\underline {= 0.12}.$$
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'''(3)'''&nbsp; Mit den bisherigen Einstellungen ist die Varianz&nbsp; $\sigma_y^2 = 0.25$&nbsp; und damit die Streuung&nbsp; $\sigma_y = 0.5$.
+
'''(3)'''&nbsp; With the previous settings,&nbsp; the variance is&nbsp; $\sigma_y^2 = 0.25$&nbsp; and thus the standard deviation&nbsp; $\sigma_y = 0.5$.
*Durch eine Verdoppelung der Koeffizienten erhält man wie gewünscht&nbsp; $\sigma_y = 1$:
+
*Doubling the coefficients gives&nbsp; $\sigma_y = 1$&nbsp; as desired:
 
:$$\hspace{0.15cm}\underline {a_0  = 0.8},\quad \hspace{0.15cm}\underline {a_1  = 0.6}.$$
 
:$$\hspace{0.15cm}\underline {a_0  = 0.8},\quad \hspace{0.15cm}\underline {a_1  = 0.6}.$$
  
  
  
'''(4)'''&nbsp; Die Konstante&nbsp; $K$&nbsp; hebt die gesamte AKF um&nbsp; $K^2$&nbsp; an.&nbsp; Mit dem Ergebnis aus&nbsp; '''(2)'''&nbsp; folgt:
+
'''(4)'''&nbsp; The constant&nbsp; $K$&nbsp; raises the total ACF by&nbsp; $K^2$.&nbsp; &nbsp; Using the result from&nbsp; '''(2)''',&nbsp; it follows:
 
:$$K^2  = 0.5 - 0.25 = 0.25\quad  \Rightarrow \quad \hspace{0.15cm}\underline {K = 0.5}.$$
 
:$$K^2  = 0.5 - 0.25 = 0.25\quad  \Rightarrow \quad \hspace{0.15cm}\underline {K = 0.5}.$$
  
  
  
'''(5)'''&nbsp; Alle AKF&ndash;Werte sind nun um den konstanten Wert&nbsp; $K^2 = 0.25$&nbsp; größer.&nbsp; Somit ist
+
'''(5)'''&nbsp; All ACF values are now larger by the constant value&nbsp; $K^2 = 0.25$.&nbsp; Thus
 
:$$\varphi _y ( { T_{\rm A} } )  =  0.12 + 0.25 \hspace{0.15cm}\underline {= 0.37},$$
 
:$$\varphi _y ( { T_{\rm A} } )  =  0.12 + 0.25 \hspace{0.15cm}\underline {= 0.37},$$
 
:$$\varphi _y ( { 2T_{\rm A} } )  =  0 + 0.25 \hspace{0.15cm}\underline {= 0.25}.$$
 
:$$\varphi _y ( { 2T_{\rm A} } )  =  0 + 0.25 \hspace{0.15cm}\underline {= 0.25}.$$
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'''(6)'''&nbsp; Durch die Konstante&nbsp; $K$&nbsp; wird die Streuung nicht verändert, das heißt, es gilt weiterhin&nbsp; $\sigma_y = 0.5$.&nbsp;  
+
'''(6)'''&nbsp; The constant&nbsp; $K$&nbsp; does not change the standard deviation, i.e.&nbsp; $\sigma_y = 0.5$&nbsp; is still valid. 
*Formal kann diese Größe auch wie folgt berechnet werden:
+
*Formally,&nbsp; this quantity can also be calculated as follows:
 
:$$\sigma _y ^2  = \varphi _y ( 0 ) - \mathop {\lim }\limits_{k \to \infty } \varphi _y ( {k \cdot T_{\rm A} } ) = 0.5 - 0.25 = 0.25.$$
 
:$$\sigma _y ^2  = \varphi _y ( 0 ) - \mathop {\lim }\limits_{k \to \infty } \varphi _y ( {k \cdot T_{\rm A} } ) = 0.5 - 0.25 = 0.25.$$
*Auch hiermit erhält man wiederum&nbsp; $\sigma_y  \hspace{0.15cm}\underline {= 0.5}$.
+
*Again,&nbsp; this gives&nbsp; $\sigma_y  \hspace{0.15cm}\underline {= 0.5}$.
  
 
{{ML-Fuß}}
 
{{ML-Fuß}}
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[[Category:Theory of Stochastic Signals: Exercises|^5.3 Filteranpassung an AKF^]]
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[[Category:Theory of Stochastic Signals: Exercises|^5.3 Filter Matching to ACF^]]

Latest revision as of 12:07, 25 February 2022

Non-recursive filter
with DC component

We consider here a first order non-recursive filter  $(M = 1)$.

  • Let the filter coefficients be  $a_0 = 0.4$  and  $a_1 = 0.3$.
  • A constant  $K$  is added at the filter output, which is to be set to zero up to and including subtask  (3)


The individual elements of the input sequence  $\left\langle \hspace{0.05cm}{x_\nu } \hspace{0.05cm}\right\rangle$

  • are Gaussian as well as mean-free,  and
  • have in each case the standard deviation  $\sigma_x = 1$.



Notes:


Questions

1

Which statements are true regarding the output ACF when  $K = 0$?    Justify your results.

The ACF value  $\varphi_y(0)$  indicates the standard deviation  $\sigma_y$.
All ACF values  $\varphi_y(k \cdot T_{\rm A})$  with  $k \ge 2$  are zero.
The power-spectral density  $\rm (PSD)$  ${\it \Phi}_y(f)$  is cosinusoidal.

2

Calculate the ACF values  $\varphi_y(k \cdot T_{\rm A})$  for  $k = 0$  and  $k = 1$.

$\varphi_y(0) \ = \ $

$\varphi_y(T_{\rm A}) \ = $

3

What values do you need to set for  $a_0$  and  $a_1$  if you want the standard deviation to be  $\sigma_y = 1$  for the same ACF shape?  Let  $a_0 > a_1$.

$a_0 \ = \ $

$a_1 \ = \ $

4

Let  $a_0 = 0.4$  and  $a_1 = 0.3$.  How large should the constant  $K$  be chosen so that  $\varphi_y(0)= 0.5$? 

$K \ = \ $

5

Using this  $K$ value,  calculate the ACF values for  $k = 1$  and  $k = 2$.

$\varphi_y(T_{\rm A}) \ = \ $

$\varphi_y(2 \cdot T_{\rm A}) \ = \ $

6

What is the standard deviation  $\sigma_y$  now?

$\sigma_y \ = \ $


Solution

(1)  Solutions 2 and 3  are correct:

  • The ACF value  $\varphi_y(0)$  gives the variance  ("power")  $\sigma_y^2$  and not the  "standard deviation"  $\sigma_y$.
  • Since a first-order non-recursive filter is present,  all ACF values are  $\varphi_y(k \cdot T_{\rm A})= 0$  for $|k| \ge 2$.
  • The ACF value  $\varphi_y(- T_{\rm A})$  is equal to  $\varphi_y(+ T_{\rm A})$.
  • These two ACF values result in a cosine function in the power-spectral density,  to which the DC component  $\varphi_y(0)$  is added.


(2)  The general equation with  $M = 1$  for  $k \in \{0, \ 1\}$ is:

$$\varphi _y ( {k \cdot T_{\rm A} } ) = \sigma _x ^2 \cdot \sum\limits_{\mu = 0}^{M - k} {a_\mu \cdot a_{\mu + k} } .$$
  • From this we obtain with  $\sigma_x = 1$:
$$\varphi _y( 0 ) = a_0 ^2 + a_1 ^2 = 0.4^2 + 0.3^2 \hspace{0.15cm}\underline { = 0.25},$$
$$\varphi _y ( { T_{\rm A} } ) = a_0 \cdot a_1 = 0.4 \cdot 0.3 \hspace{0.15cm}\underline {= 0.12}.$$


(3)  With the previous settings,  the variance is  $\sigma_y^2 = 0.25$  and thus the standard deviation  $\sigma_y = 0.5$.

  • Doubling the coefficients gives  $\sigma_y = 1$  as desired:
$$\hspace{0.15cm}\underline {a_0 = 0.8},\quad \hspace{0.15cm}\underline {a_1 = 0.6}.$$


(4)  The constant  $K$  raises the total ACF by  $K^2$.    Using the result from  (2),  it follows:

$$K^2 = 0.5 - 0.25 = 0.25\quad \Rightarrow \quad \hspace{0.15cm}\underline {K = 0.5}.$$


(5)  All ACF values are now larger by the constant value  $K^2 = 0.25$.  Thus

$$\varphi _y ( { T_{\rm A} } ) = 0.12 + 0.25 \hspace{0.15cm}\underline {= 0.37},$$
$$\varphi _y ( { 2T_{\rm A} } ) = 0 + 0.25 \hspace{0.15cm}\underline {= 0.25}.$$


(6)  The constant  $K$  does not change the standard deviation, i.e.  $\sigma_y = 0.5$  is still valid.

  • Formally,  this quantity can also be calculated as follows:
$$\sigma _y ^2 = \varphi _y ( 0 ) - \mathop {\lim }\limits_{k \to \infty } \varphi _y ( {k \cdot T_{\rm A} } ) = 0.5 - 0.25 = 0.25.$$
  • Again,  this gives  $\sigma_y \hspace{0.15cm}\underline {= 0.5}$.