Difference between revisions of "Aufgaben:Exercise 5.6: Filter Dimensioning"

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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_ID566__Sto_A_5_6.png|right|frame|Gewünschte AKF  $\varphi_y(k \cdot T_{\rm A})$]]
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[[File:P_ID566__Sto_A_5_6.png|right|frame|Desired ACF  $\varphi_y(k \cdot T_{\rm A})$]]
Eine zeitdiskrete Zufallsgröße  $\left\langle \hspace{0.05cm}{y_\nu  } \hspace{0.05cm}\right\rangle$  mit der skizzierten AKF soll mit Hilfe eines digitalen Filters erzeugt werden.
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A discrete-time random variable  $\left\langle \hspace{0.05cm}{y_\nu  } \hspace{0.05cm}\right\rangle$  with the outlined auto-correlation function  $\rm (ACF)$  is to be generated using a digital filter.
  
Die zeitdiskreten Gaußschen Eingangswerte  $x_\nu$  seien  jeweils gekennzeichnet durch
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Let the discrete-time Gaussian input values  $x_\nu$  be characterized in each case by
*den Mittelwert  $m_x = 0$,  
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*the mean value  $m_x = 0$,  
*die Streuung  $\sigma_x = 1$.
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*the standard deviation  $\sigma_x = 1$.
  
  
  
  
 
+
Notes:  
 
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*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Creation_of_Predefined_ACF_Properties|Creation of Predefined ACF Properties]].
 
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*Reference is also made to the chapter  [[Theory_of_Stochastic_Signals/Auto-Correlation_Function_(ACF)|Auto-Correlation Function]].
 
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*Let all ACF values  $\varphi_y(k \cdot T_{\rm A})$  with index  $|k| \gt 2$  be zero.
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel  [[Theory_of_Stochastic_Signals/Erzeugung_vorgegebener_AKF-Eigenschaften|Erzeugung vorgegebener AKF-Eigenschaften]].
 
*Bezug genommen wird auch auf das Kapitel  [[Theory_of_Stochastic_Signals/Autokorrelationsfunktion_(AKF)|Autokorrelationsfunktion]].
 
*Alle AKF-Werte  $\varphi_y(k \cdot T_{\rm A})$  mit Index  $|k| \gt 2$  seien Null.
 
 
   
 
   
  
  
===Fragebogen===
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===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche der folgenden Aussagen sind zutreffend?
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{Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
- Es eignet sich ein rekursives Filter erster Ordnung.
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- A first-order recursive filter is suitable.
- Es eignet sich ein nichtrekursives Filter erster Ordnung.
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- A first-order non-recursive filter is suitable.
+ Es eignet sich ein nichtrekursives Filter zweiter Ordnung.
+
+ A second order non-recursive filter is suitable.
- Die Ausgangswerte&nbsp; $y_\nu$&nbsp; sind dreieckverteilt.
+
- The output values&nbsp; $y_\nu$&nbsp; are triangularly distributed.
+ Die Ausgangswerte&nbsp; $y_\nu$&nbsp; sind mittelwertfrei&nbsp; $(m_y = 0)$.
+
+ The output values&nbsp; $y_\nu$&nbsp; are mean-free&nbsp; $(m_y = 0)$.
  
  
  
{Geben Sie die Gleichungen zur Bestimmung der Koeffizienten&nbsp; $a_0$,&nbsp; $a_1$&nbsp; und&nbsp; $a_2$&nbsp; an.&nbsp; Ersetzen Sie die drei Variablen durch&nbsp; $u = a_1^2$&nbsp; und&nbsp; $w = (a_0 + a_2)^2$. <br>Bestimmen Sie&nbsp; $u$&nbsp; und&nbsp; $w$.&nbsp; <i>Hinweis:</i> &nbsp;Es gibt nur eine sinnvolle Lösung.
+
{Give the equations for determining the coefficients&nbsp; $a_0$,&nbsp; $a_1$&nbsp; and&nbsp; $a_2$.&nbsp; &nbsp; Replace the three variables with&nbsp; $u = a_1^2$&nbsp; and&nbsp; $w = (a_0 + a_2)^2$.&nbsp; Determine&nbsp; $u$&nbsp; and&nbsp; $w$.&nbsp; <br> &nbsp; &nbsp; &nbsp; <u>Note:</u> &nbsp;There is only one reasonable solution.
 
|type="{}"}
 
|type="{}"}
 
$u \ = \ $ { 0.25 3% }
 
$u \ = \ $ { 0.25 3% }
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{Bestimmen Sie die Filterkoeffizienten&nbsp; $a_0$,&nbsp; $a_1$&nbsp; und&nbsp; $a_2$.&nbsp; Geben Sie die folgenden Quotienten ein:
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{Determine the filter coefficients&nbsp; $a_0$,&nbsp; $a_1$&nbsp; and&nbsp; $a_2$.&nbsp; Enter the following quotients:
 
|type="{}"}
 
|type="{}"}
 
$a_1/a_0 \ =  \ $ { -0.515--0.485 }
 
$a_1/a_0 \ =  \ $ { -0.515--0.485 }
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{Wieviele verschiedene Parametersätze&nbsp; $(I)$&nbsp; führen zur gewünschten AKF?
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{How many different sets of parameters&nbsp; $(I)$&nbsp; lead to the desired ACF?
 
|type="{}"}
 
|type="{}"}
 
$I \ =  \ $ { 2 }
 
$I \ =  \ $ { 2 }
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Richtig sind <u>die Lösungsvorschläge 3 und 5</u>:
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'''(1)'''&nbsp; <u>Solutions 3 and 5</u>&nbsp; are correct:
*Ein rekursives Filter würde stets eine unendlich weit ausgedehnte Impulsantwort&nbsp; $h(t)$&nbsp; und damit auch eine unendlich ausgedehnte AKF bewirken.  
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*A recursive filter would always cause an infinitely extended impulse response&nbsp; $h(t)$&nbsp; and thus also an infinitely extended auto-correlation function&nbsp; $\rm (ACF)$.
*Deshalb ist hier eine nichtrekursive Filterstruktur zu wählen.&nbsp; Die angegebene AKF erfordert die Ordnung&nbsp; $M= 2$.
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*Therefore,&nbsp; a non-recursive filter structure must be chosen here.&nbsp; The specified ACF requires the order&nbsp; $M= 2$.
*Da die Eingangswerte gaußverteilt und mittelwertfrei sind, gilt dies auch für die Ausgangswerte.  
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*Since the input values are Gaussian distributed and mean-free,&nbsp; this also applies to the output values.  
*Bei der Filterung stochastischer Signale gilt stets:&nbsp; "Gauß bleibt Gauß und Nicht-Gauß wird nie (exakt) Gauß".  
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*When filtering stochastic signals,&nbsp; the following always applies:&nbsp; '''Gaussian remains Gaussian and non-Gaussian never becomes (exactly) Gaussian'''.
  
  
  
'''(2)'''&nbsp; Das Gleichungssystem lautet:
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'''(2)'''&nbsp; The system of equations is:
 
:$$k = 2\text{:}\quad a_0  \cdot a_2  = 1.$$
 
:$$k = 2\text{:}\quad a_0  \cdot a_2  = 1.$$
 
:$$k = 1\text{:}\quad a_0  \cdot a_1  + a_1  \cdot a_2  =  - 1\quad  \Rightarrow \quad \sqrt {u \cdot w}  =  - 1\quad  \Rightarrow \quad u \cdot w = 1.$$
 
:$$k = 1\text{:}\quad a_0  \cdot a_1  + a_1  \cdot a_2  =  - 1\quad  \Rightarrow \quad \sqrt {u \cdot w}  =  - 1\quad  \Rightarrow \quad u \cdot w = 1.$$
 
:$$k = 0\text{:}\quad a_0 ^2  + a_1 ^2  + a_2 ^2  = 2.25\quad \;\;\,  \Rightarrow \quad u + w = 2.25 + 2a_0  \cdot a_2  = 4.25.$$
 
:$$k = 0\text{:}\quad a_0 ^2  + a_1 ^2  + a_2 ^2  = 2.25\quad \;\;\,  \Rightarrow \quad u + w = 2.25 + 2a_0  \cdot a_2  = 4.25.$$
  
Das Gleichungssystem bezüglich&nbsp; $u$&nbsp; und&nbsp; $w$&nbsp; hat zwei Lösungen:
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The two equations with respect to&nbsp; $u$&nbsp; and&nbsp; $w$&nbsp; has two solutions:
*$u = 4, \ w = 0.25$: &nbsp; Wegen der Bedingung&nbsp; $a_2 = 1/a_0$&nbsp; (siehe erste Gleichung) haben&nbsp; $a_0$&nbsp; und&nbsp; $a_2$&nbsp; gleiches Vorzeichen.
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*$u = 4, \ w = 0.25$: &nbsp; Because of the condition&nbsp; $a_2 = 1/a_0$&nbsp; (see first equation),&nbsp; $a_0$&nbsp; and&nbsp; $a_2$&nbsp; have the same sign.
* Außerdem ist mindestens einer der beiden Koeffizienten  größer/gleich&nbsp; $1$.  
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* Moreover,&nbsp; at least one of the two coefficients is greater than/equal to&nbsp; $1$.  
*Somit ist die Bedingung&nbsp; $a_0+a_2= \sqrt{w} = 0.5$&nbsp; nicht zu erfüllen.
+
*Thus the condition&nbsp; $a_0+a_2= \sqrt{w} = 0.5$&nbsp; cannot be fulfilled.
*Die richtige Lösung lautet deshalb&nbsp; $\underline{u = 0.25}, \ \underline{w = 4}$.
+
*Therefore,&nbsp; the correct solution is&nbsp; $\underline{u = 0.25}, \ \underline{w = 4}$.
  
  
  
'''(3)'''&nbsp; Das Ergebnis von&nbsp; '''(2)'''&nbsp; bedeutet, dass&nbsp; $a_1 = \pm \sqrt{0.25} = \pm 0.5$&nbsp; ist.
+
'''(3)'''&nbsp; The result of&nbsp; '''(2)'''&nbsp; means that&nbsp; $a_1 = \pm \sqrt{0.25} = \pm 0.5$.&nbsp;  
*Der positive Wert führt zum Gleichungssystem
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*The positive value leads to the equations
$$(1) \hspace{0.5cm}0.5 \cdot \left( {a_0  + a_2 } \right) =  - 1\quad  \Rightarrow \quad a_0  + a_2  =  - 2,$$
+
:$$(1) \hspace{0.5cm}0.5 \cdot \left( {a_0  + a_2 } \right) =  - 1\quad  \Rightarrow \quad a_0  + a_2  =  - 2,$$
$$(2) \hspace{0.5cm}a_0  \cdot a_2  = 1.$$
+
:$$(2) \hspace{0.5cm}a_0  \cdot a_2  = 1.$$
  
*Daraus folgt&nbsp; $a_0=a_2=-1$.&nbsp; Mit&nbsp; $a_1= 0.5$&nbsp; erhält man als Endergebnis:
+
*From this follows&nbsp; $a_0=a_2=-1$.&nbsp; With&nbsp; $a_1= 0.5$,&nbsp; the final result is:
 
:$$a_1/a_0  \hspace{0.15 cm}\underline{= -0.5},  \hspace{0.5 cm}
 
:$$a_1/a_0  \hspace{0.15 cm}\underline{= -0.5},  \hspace{0.5 cm}
 
a_2/a_0  \hspace{0.15 cm}\underline{= 1}.$$
 
a_2/a_0  \hspace{0.15 cm}\underline{= 1}.$$
  
*Die Lösung &nbsp;$a_1= -0.5$&nbsp; führt zu &nbsp;$a_0=a_2=+1$&nbsp; und damit zu den gleichen Quotienten.
+
*The solution &nbsp;$a_1= -0.5$&nbsp; leads to &nbsp;$a_0=a_2=+1$&nbsp; and thus to the same quotients.
  
  
  
'''(4)'''&nbsp; Allgemein hat dieses Problem&nbsp; $I = 4$&nbsp; äquivalente Lösungen&nbsp; $($Spiegelung/Verschiebung sowie jeweils die Multiplikation mit&nbsp; $-1)$.  
+
'''(4)'''&nbsp; In general,&nbsp; this problem has&nbsp; $I = 4$&nbsp; equivalent solutions&nbsp; $($mirroring/shifting as well as the multiplication by&nbsp; $-1$&nbsp; in each case$)$.  
*Da hier die Impulsantwort symmetrisch ist, gibt es allerdings nur&nbsp; $\underline{I = 2}$&nbsp; unterschiedliche Lösungen:
+
*Since here the impulse response is symmetrical,&nbsp; there are however only&nbsp; $\underline{I = 2}$&nbsp; different solutions:
:$$\text{Lösung 1:} \ \ a_0  = +1,\quad a_1  =  - 0.5,\quad a_2  = +1; $$
+
:$$\text{Solution 1:} \ \ a_0  = +1,\quad a_1  =  - 0.5,\quad a_2  = +1; $$
:$$\text{Lösung 2:} \ \ a_0  =  - 1,\quad a_1  = +0.5,\quad a_2  =  - 1. $$
+
:$$\text{Solution 2:} \ \ a_0  =  - 1,\quad a_1  = +0.5,\quad a_2  =  - 1. $$
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[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 17:22, 17 February 2022

Desired ACF  $\varphi_y(k \cdot T_{\rm A})$

A discrete-time random variable  $\left\langle \hspace{0.05cm}{y_\nu } \hspace{0.05cm}\right\rangle$  with the outlined auto-correlation function  $\rm (ACF)$  is to be generated using a digital filter.

Let the discrete-time Gaussian input values  $x_\nu$  be characterized in each case by

  • the mean value  $m_x = 0$,
  • the standard deviation  $\sigma_x = 1$.



Notes:


Questions

1

Which of the following statements are true?

A first-order recursive filter is suitable.
A first-order non-recursive filter is suitable.
A second order non-recursive filter is suitable.
The output values  $y_\nu$  are triangularly distributed.
The output values  $y_\nu$  are mean-free  $(m_y = 0)$.

2

Give the equations for determining the coefficients  $a_0$,  $a_1$  and  $a_2$.    Replace the three variables with  $u = a_1^2$  and  $w = (a_0 + a_2)^2$.  Determine  $u$  and  $w$. 
      Note:  There is only one reasonable solution.

$u \ = \ $

$w \ = \ $

3

Determine the filter coefficients  $a_0$,  $a_1$  and  $a_2$.  Enter the following quotients:

$a_1/a_0 \ = \ $

$a_2/a_0 \ = \ $

4

How many different sets of parameters  $(I)$  lead to the desired ACF?

$I \ = \ $


Solution

(1)  Solutions 3 and 5  are correct:

  • A recursive filter would always cause an infinitely extended impulse response  $h(t)$  and thus also an infinitely extended auto-correlation function  $\rm (ACF)$.
  • Therefore,  a non-recursive filter structure must be chosen here.  The specified ACF requires the order  $M= 2$.
  • Since the input values are Gaussian distributed and mean-free,  this also applies to the output values.
  • When filtering stochastic signals,  the following always applies:  Gaussian remains Gaussian and non-Gaussian never becomes (exactly) Gaussian.


(2)  The system of equations is:

$$k = 2\text{:}\quad a_0 \cdot a_2 = 1.$$
$$k = 1\text{:}\quad a_0 \cdot a_1 + a_1 \cdot a_2 = - 1\quad \Rightarrow \quad \sqrt {u \cdot w} = - 1\quad \Rightarrow \quad u \cdot w = 1.$$
$$k = 0\text{:}\quad a_0 ^2 + a_1 ^2 + a_2 ^2 = 2.25\quad \;\;\, \Rightarrow \quad u + w = 2.25 + 2a_0 \cdot a_2 = 4.25.$$

The two equations with respect to  $u$  and  $w$  has two solutions:

  • $u = 4, \ w = 0.25$:   Because of the condition  $a_2 = 1/a_0$  (see first equation),  $a_0$  and  $a_2$  have the same sign.
  • Moreover,  at least one of the two coefficients is greater than/equal to  $1$.
  • Thus the condition  $a_0+a_2= \sqrt{w} = 0.5$  cannot be fulfilled.
  • Therefore,  the correct solution is  $\underline{u = 0.25}, \ \underline{w = 4}$.


(3)  The result of  (2)  means that  $a_1 = \pm \sqrt{0.25} = \pm 0.5$. 

  • The positive value leads to the equations
$$(1) \hspace{0.5cm}0.5 \cdot \left( {a_0 + a_2 } \right) = - 1\quad \Rightarrow \quad a_0 + a_2 = - 2,$$
$$(2) \hspace{0.5cm}a_0 \cdot a_2 = 1.$$
  • From this follows  $a_0=a_2=-1$.  With  $a_1= 0.5$,  the final result is:
$$a_1/a_0 \hspace{0.15 cm}\underline{= -0.5}, \hspace{0.5 cm} a_2/a_0 \hspace{0.15 cm}\underline{= 1}.$$
  • The solution  $a_1= -0.5$  leads to  $a_0=a_2=+1$  and thus to the same quotients.


(4)  In general,  this problem has  $I = 4$  equivalent solutions  $($mirroring/shifting as well as the multiplication by  $-1$  in each case$)$.

  • Since here the impulse response is symmetrical,  there are however only  $\underline{I = 2}$  different solutions:
$$\text{Solution 1:} \ \ a_0 = +1,\quad a_1 = - 0.5,\quad a_2 = +1; $$
$$\text{Solution 2:} \ \ a_0 = - 1,\quad a_1 = +0.5,\quad a_2 = - 1. $$