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

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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_ID567__Sto_Z_5_6.png|right|]]
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[[File:P_ID567__Sto_Z_5_6.png|right|frame|Desired ACF  $\varphi_y(k \cdot T_{\rm A})$]]
:Mit Hilfe eines nichtrekursiven digitalen Filters erster Ordnung soll eine zeitdiskrete Zufallsgröße &#9001;<i>y<sub>&nu;</sub></i>&#9002; generiert werden, die folgende AKF-Werte aufweist:
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Using a first-order non-recursive digital filter,&nbsp; generate a discrete-time random sequence&nbsp; $\left\langle \hspace{0.05cm} {y_\nu } \hspace{0.05cm} \right\rangle$&nbsp; that has the following ACF values:
:$$\varphi _y ( {k \cdot T_{\rm A} } ) = \left\{ {\begin{array}{*{20}c}  {\varphi _0  = 1} & {\rm  f\ddot{u}r} & {k = 0}  \\  {\varphi _1 } & {\rm f\ddot{u}r} & {\left| k \right| = 1}  \\  0 & {} & {{\rm{sonst}}.}  \\ \end{array}} \right.$$
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:$$\varphi _y ( {k \cdot T_{\rm A} } ) = \left\{ {\begin{array}{*{20}c}  {\varphi _0  = 1} & {\rm  for} & {k = 0}  \\  {\varphi _1 } & {\rm for} & {\left| k \right| = 1}  \\  0 & {} & {{\rm{otherwise}}.}  \\ \end{array}} \right.$$
  
:<br>Hierbei bezeichnet <i>&phi;</i><sub>1</sub> einen (in bestimmten Grenzen) frei wählbaren Parameter. Weiter gelte:
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Here&nbsp; $\varphi_1$&nbsp; denotes a parameter that can be freely chosen&nbsp; (within certain limits).
  
:* Die zeitdiskreten Eingangswerte <i>x<sub>&nu;</sub></i> sind gaußverteilt mit Mittelwert <i>m<sub>x</sub></i> und Streuung <i>&sigma;<sub>x</sub></i>.
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Further,&nbsp; it holds:
  
:* Zunächst sei <i>m<sub>x</sub></i> = 0 und <i>&sigma;<sub>x</sub></i> = 1.
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* The discrete-time input values&nbsp; $x_\nu$&nbsp; are Gaussian distributed with mean&nbsp; $m_x$&nbsp; and standard deviation&nbsp; $\sigma_x$.
 +
* For the whole exercise&nbsp; $\sigma_x= 1$&nbsp; is valid.&nbsp; The mean value is initially&nbsp; $m_x = 0$.
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*In the subtask&nbsp; '''(4)'''&nbsp; &nbsp;  $m_x = 1$&nbsp; is valid.
  
:Damit lautet das Gleichungssystem zur Bestimmung der Filterkoeffizienten <i>a</i><sub>0</sub> und <i>a</i><sub>1</sub>:
 
:$$a_0 ^2  + a_1 ^2  = 1,$$
 
:$$a_0  \cdot a_1  = \varphi _1 .$$
 
  
:<b>Hinweis:</b> Diese Aufgabe bezieht sich auf den Theorieteil von Kapitel 5.3.
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Thus the system of equations for the determination of the filter coefficients&nbsp; $a_0$&nbsp; and&nbsp; $a_1$ is:
 +
:$$a_0 ^2  + a_1 ^2  = 1, $$
 +
:$$a_0  \cdot a_1  = \varphi_1 .$$
  
===Fragebogen===
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 +
 
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 +
 
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Notes:
 +
*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 chapter&nbsp; [[Theory_of_Stochastic_Signals/Auto-Correlation_Function_(ACF)|Auto-Correlation Function]].
 +
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Wie lauten die Grenzen für <i>&phi;</i><sub>1</sub>, damit das Gleichungssystem lösbar ist?
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{What are the allowable limits for&nbsp; $\varphi_1$,&nbsp; so that the system of equations is solvable?
 
|type="{}"}
 
|type="{}"}
$\phi_\text{1,max}$ = { 0.5 3% }
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$\varphi_\text{1, max} \ = \ $ { 0.5 3% }
$\phi_\text{1,min}$ = - { 0.5 3% }
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$\varphi_\text{1, min} \ =  \ $ { -0.515--0.485 }
  
  
{Es gelte <i>&phi;</i><sub>1</sub> = &ndash;0.3. Bestimmen Sie die Filterparameter <i>a</i><sub>0</sub> und <i>a</i><sub>1</sub>. Wählen Sie die Lösung mit positivem <i>a</i><sub>0</sub> und |<i>a</i><sub>0</sub>| &gt; |<i>a</i><sub>1</sub>|.
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{Let&nbsp; $\varphi_1= -0.3$.&nbsp; Determine the filter parameters&nbsp; $a_0$&nbsp; and&nbsp; $a_1$.&nbsp; Choose the solution with positive&nbsp; $a_0$&nbsp; and&nbsp; $|a_1| < a_0$.
 
|type="{}"}
 
|type="{}"}
$a_0$ = { 0.949 3% }
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$a_0 \ =  \ $ { 0.949 3% }
$a_1$ = - { 0.316 3% }
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$a_1 \ =  \ ${ -0.326--0.306 }
  
  
{Wie ändert sich die AKF, wenn bei gleichen Filterkoeffizienten nun <i>&sigma;<sub>x</sub></i> = 2 gilt? Wie groß ist insbesondere der AKF&ndash;Wert für <i>k</i> = 1?
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{How does the ACF change if now&nbsp; $\sigma_x = 2$&nbsp; with the same filter coefficients?&nbsp; In particular,&nbsp; what is the value of the ACF for&nbsp; $k = 1 $?
 
|type="{}"}
 
|type="{}"}
$\phi_y(T_A)$ = - { 1.2 3% }
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$\varphi_y(T_{\rm A}) \ =  \ $ { -1.236--1.164 }
  
  
{Wie ändert sich die AKF bei gleichen Filterkoeffizienten und <i>&sigma;<sub>x</sub></i> = 2 mit einem Gleichanteil <i>m<sub>x</sub></i> = 1? Wie groß ist nun der AKF-Wert für <i>k</i> = 1?<br />
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{How does the ACF change with the same filter coefficients and&nbsp; $\sigma_x = 2$&nbsp; with a DC component&nbsp; $m_x = 1$?&nbsp; Now what is the ACF value for&nbsp; $k = 1 $?
 
|type="{}"}
 
|type="{}"}
$\phi_y(T_A)$ = - { 0.8 3% }
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$\varphi_y(T_{\rm A}) \ =  \ $ { -0.82--0.78 }
  
  
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
:<b>1.</b>&nbsp;&nbsp;Nach einigen Umformungen kommt man zur Bestimmungsgleichung (mit <i>u</i> = <i>a</i><sub>0</sub><sup>2</sup>):
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'''(1)'''&nbsp; After some transformations we arrive at the equation of determination&nbsp; $($with&nbsp; $u = a_0^2)$:
 
:$$a_0 \cdot a_1 = \varphi_1 \hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
:$$a_0 \cdot a_1 = \varphi_1 \hspace{0.3cm}\Rightarrow\hspace{0.3cm}
 
  a_1 = \varphi_1 /a_0 ,$$
 
  a_1 = \varphi_1 /a_0 ,$$
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u^2 - u + \varphi_1^2  = 0.$$
 
u^2 - u + \varphi_1^2  = 0.$$
  
:Dies führt zu den beiden Lösungen:
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*This leads to the two solutions:
 
:$$u_{1/2}  = 0.5 \pm \sqrt {0.25 - \varphi _1 ^2 } .$$
 
:$$u_{1/2}  = 0.5 \pm \sqrt {0.25 - \varphi _1 ^2 } .$$
  
:Reelle Lösungen gibt es nur für <i>&phi;</i><sub>1</sub><sup>2</sup> &le; 0.25. Das bedeutet:
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*Real solutions exist only for&nbsp; $\varphi_1^2 \le 0.25$,&nbsp; which means:
:$$\hspace{0.15cm}\underline {\varphi _{1,\max }  = +0.5}, \quad \hspace{0.15cm}\underline {\varphi _{1,\min }  =  - 0.5}.$$
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:$$\hspace{0.15cm}\underline {\varphi_\text{1, max}  = +0.5}, \quad \hspace{0.15cm}\underline {\varphi_\text{1, min}  =  - 0.5}.$$
 +
 
  
:<b>2.</b>&nbsp;&nbsp;Mit <i>&phi;</i><sub>1</sub> = &ndash;0.3 erhält man <i>u</i><sub>1</sub> = 0.9 und <i>u</i><sub>2</sub> = 0.1. Daraus ergeben sich folgende Parametersätze:
 
:$$a_0  = \;\;\,\sqrt {0.9}  = \;\;\, 0.949,\quad a_1  =  - \sqrt {0.1}  =  - 0.316;$$
 
:$$a_0  =  - \sqrt {0.9}  =  - 0.949,\quad a_1  = \;\;\, \sqrt {0.1}  = \;\;\, 0.316;$$
 
:$$a_0  = \;\;\, \sqrt {0.1}  = \;\;\, 0.316,\quad a_1  =  - \sqrt {0.9}  =  - 0.949;$$
 
:$$a_0  =  - \sqrt {0.1}  =  - 0.316,\quad a_1  = \;\;\, \sqrt {0.9}  = \;\;\, 0.949.$$
 
  
:Nur der erste Parametersatz erfüllt die angegebene Nebenbedingung: <u><i>a</i><sub>0</sub> = 0.949</u> und <u><i>a</i><sub>1</sub> = &ndash;0.316</u>.
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'''(2)'''&nbsp; With&nbsp; $\varphi_1=-0.3$,&nbsp; we get&nbsp; $u_1 = 0.9$&nbsp; and&nbsp; $u_2 = 0.1$&nbsp; resulting in the following sets of parameters:
 +
:$$\text{Solution 1:} \ \ a_0  = \;\;\,\sqrt {0.9}  = \;\;\, 0.949,\quad a_1  =  - \sqrt {0.1= - 0.316;$$
 +
:$$\text{Solution 2:} \ \ a_0  =  - \sqrt {0.9}  =  - 0.949,\quad a_1  = \;\;\, \sqrt {0.1}  = \;\;\, 0.316;$$
 +
:$$\text{Solution 3:} \ \ a_0  = \;\;\, \sqrt {0.1}  = \;\;\, 0.316,\quad a_1  =  - \sqrt {0.9}  =  - 0.949;$$
 +
:$$\text{Solution 4:} \ \ a_0  =  - \sqrt {0.1}  =  - 0.316,\quad a_1  = \;\;\, \sqrt {0.9}  = \;\;\, 0.949.$$
  
:<b>3.</b>&nbsp;&nbsp;Wird <i>&sigma;<sub>x</sub></i> verdoppelt, so erhöhen sich alle AKF-Werte um den Faktor 4. Insbesondere gilt dann:
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*Only the first parameter set satisfies the specified constraint:
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:$$a_0 \hspace{0.15cm}\underline {= 0.949} \ \text{  und  } \ a_1 \hspace{0.15cm}\underline {= -0.316}.$$
 +
 
 +
 
 +
'''(3)'''&nbsp; If&nbsp; $\sigma_x$&nbsp; is doubled,&nbsp; all ACF values increase by a factor of&nbsp; $4$.&nbsp; In particular,&nbsp; then holds:
 
:$$\varphi _y( {T_{\rm A} } ) =  - 0.3 \cdot 4 \hspace{0.15cm}\underline{=  - 1.2}.$$
 
:$$\varphi _y( {T_{\rm A} } ) =  - 0.3 \cdot 4 \hspace{0.15cm}\underline{=  - 1.2}.$$
  
:<b>4.</b>&nbsp;&nbsp;Der Gleichanteil <i>m<sub>x</sub></i> = 1 am Eingang führt zu folgendem Gleichanteil im Ausgangssignal:
 
:$$m_y  = m_x \cdot  ( {a_0  + a_1 } ) = 0.633.$$
 
  
:Alle AKF-Werte werden deshalb gegenüber Punkt (c) um <i>m<sub>y</sub></i><sup>2</sup> &asymp; 0.4 vergrößert und man erhält:
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 +
'''(4)'''&nbsp; The DC component&nbsp; $m_x = 1$&nbsp; at the input leads to the following DC component in the output signal:
 +
:$$m_y  = m_x \cdot  ( {a_0  + a_1 } ) = 1 \cdot (0.949 -0.316) = 0.633.$$
 +
 
 +
*All ACF values are therefore increased by&nbsp; $m_y^2  \approx 0.4$&nbsp; compared to subtask&nbsp; '''(3)'''&nbsp; and we now obtain:
 
:$$\varphi _y( {T_{\rm A} } )\hspace{0.15cm}\underline{ \approx  - 0.8}.$$
 
:$$\varphi _y( {T_{\rm A} } )\hspace{0.15cm}\underline{ \approx  - 0.8}.$$
 
{{ML-Fuß}}
 
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[[Category:Aufgaben zu Stochastische Signaltheorie|^5.3 Erzeugung vorgegebener AKF-Eigenschaften^]]
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[[Category:Theory of Stochastic Signals: Exercises|^5.3 Filter Matching to ACF^]]

Latest revision as of 12:34, 21 February 2022

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

Using a first-order non-recursive digital filter,  generate a discrete-time random sequence  $\left\langle \hspace{0.05cm} {y_\nu } \hspace{0.05cm} \right\rangle$  that has the following ACF values:

$$\varphi _y ( {k \cdot T_{\rm A} } ) = \left\{ {\begin{array}{*{20}c} {\varphi _0 = 1} & {\rm for} & {k = 0} \\ {\varphi _1 } & {\rm for} & {\left| k \right| = 1} \\ 0 & {} & {{\rm{otherwise}}.} \\ \end{array}} \right.$$

Here  $\varphi_1$  denotes a parameter that can be freely chosen  (within certain limits).

Further,  it holds:

  • The discrete-time input values  $x_\nu$  are Gaussian distributed with mean  $m_x$  and standard deviation  $\sigma_x$.
  • For the whole exercise  $\sigma_x= 1$  is valid.  The mean value is initially  $m_x = 0$.
  • In the subtask  (4)    $m_x = 1$  is valid.


Thus the system of equations for the determination of the filter coefficients  $a_0$  and  $a_1$ is:

$$a_0 ^2 + a_1 ^2 = 1, $$
$$a_0 \cdot a_1 = \varphi_1 .$$




Notes:



Questions

1

What are the allowable limits for  $\varphi_1$,  so that the system of equations is solvable?

$\varphi_\text{1, max} \ = \ $

$\varphi_\text{1, min} \ = \ $

2

Let  $\varphi_1= -0.3$.  Determine the filter parameters  $a_0$  and  $a_1$.  Choose the solution with positive  $a_0$  and  $|a_1| < a_0$.

$a_0 \ = \ $

$a_1 \ = \ $

3

How does the ACF change if now  $\sigma_x = 2$  with the same filter coefficients?  In particular,  what is the value of the ACF for  $k = 1 $?

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

4

How does the ACF change with the same filter coefficients and  $\sigma_x = 2$  with a DC component  $m_x = 1$?  Now what is the ACF value for  $k = 1 $?

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


Solution

(1)  After some transformations we arrive at the equation of determination  $($with  $u = a_0^2)$:

$$a_0 \cdot a_1 = \varphi_1 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} a_1 = \varphi_1 /a_0 ,$$
$$a_0^2 + a_1^2 = 1 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} a_0^2 + \varphi_1^2 /a_0^2 -1 = 0,$$
$$u = a_0^2 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} u + \varphi_1^2 /u -1 = 0 \hspace{0.3cm}\Rightarrow\hspace{0.3cm} u^2 - u + \varphi_1^2 = 0.$$
  • This leads to the two solutions:
$$u_{1/2} = 0.5 \pm \sqrt {0.25 - \varphi _1 ^2 } .$$
  • Real solutions exist only for  $\varphi_1^2 \le 0.25$,  which means:
$$\hspace{0.15cm}\underline {\varphi_\text{1, max} = +0.5}, \quad \hspace{0.15cm}\underline {\varphi_\text{1, min} = - 0.5}.$$


(2)  With  $\varphi_1=-0.3$,  we get  $u_1 = 0.9$  and  $u_2 = 0.1$  resulting in the following sets of parameters:

$$\text{Solution 1:} \ \ a_0 = \;\;\,\sqrt {0.9} = \;\;\, 0.949,\quad a_1 = - \sqrt {0.1} = - 0.316;$$
$$\text{Solution 2:} \ \ a_0 = - \sqrt {0.9} = - 0.949,\quad a_1 = \;\;\, \sqrt {0.1} = \;\;\, 0.316;$$
$$\text{Solution 3:} \ \ a_0 = \;\;\, \sqrt {0.1} = \;\;\, 0.316,\quad a_1 = - \sqrt {0.9} = - 0.949;$$
$$\text{Solution 4:} \ \ a_0 = - \sqrt {0.1} = - 0.316,\quad a_1 = \;\;\, \sqrt {0.9} = \;\;\, 0.949.$$
  • Only the first parameter set satisfies the specified constraint:
$$a_0 \hspace{0.15cm}\underline {= 0.949} \ \text{ und } \ a_1 \hspace{0.15cm}\underline {= -0.316}.$$


(3)  If  $\sigma_x$  is doubled,  all ACF values increase by a factor of  $4$.  In particular,  then holds:

$$\varphi _y( {T_{\rm A} } ) = - 0.3 \cdot 4 \hspace{0.15cm}\underline{= - 1.2}.$$


(4)  The DC component  $m_x = 1$  at the input leads to the following DC component in the output signal:

$$m_y = m_x \cdot ( {a_0 + a_1 } ) = 1 \cdot (0.949 -0.316) = 0.633.$$
  • All ACF values are therefore increased by  $m_y^2 \approx 0.4$  compared to subtask  (3)  and we now obtain:
$$\varphi _y( {T_{\rm A} } )\hspace{0.15cm}\underline{ \approx - 0.8}.$$