Difference between revisions of "Aufgaben:Exercise 3.9Z: Convolution of Gaussian Pulses"

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{{quiz-Header|Buchseite=Signaldarstellung/Faltungssatz und Faltungsoperation
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{{quiz-Header|Buchseite=Signal Representation/The Convolution Theorem and Operation
 
}}
 
}}
  
[[File:P_ID544__Sig_Z_3_9.png|right|frame|Gaußförmige $x(t)$ und $h(t)$]]
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[[File:P_ID544__Sig_Z_3_9.png|right|frame|Gaussian pulses   $x(t)$,   $h(t)$]]
Es soll das Faltungsergebnis zweier Gaußfunktionen ermittelt werden. Wir betrachten einen gaußförmigen Eingangsimpuls  ${x(t)}$  mit Amplitude $x_0 = 1\,\text{V}$ und äquivalenter Dauer  $\Delta t_x = 4 \,\text{ms}$  sowie eine ebenfalls gaußförmige Impulsantwort  ${h(t)}$, welche die äquivalente Dauer  $\Delta t_h = 3 \,\text{ms}$  aufweist:
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The convolution result of two Gaussian functions is to be determined.  We consider
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*a Gaussian input pulse  ${x(t)}$  with amplitude $x_0 = 1\,\text{V}$ and  "equivalent pulse duration"  $\Delta t_x = 4 \,\text{ms}$,  as well as
 +
*a likewise Gaussian impulse response  ${h(t)}$, which has the  "equivalent pulse duration"  $\Delta t_h = 3 \,\text{ms}$ :
 
:$$x( t ) = x_0  \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_x } )^2 } ,$$
 
:$$x( t ) = x_0  \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_x } )^2 } ,$$
 
:$$h( t ) = \frac{1}{\Delta t_h } \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_h } )^2 } .$$
 
:$$h( t ) = \frac{1}{\Delta t_h } \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_h } )^2 } .$$
Gesucht ist das Ausgangssignal  ${y(t)} = {x(t)} ∗{h(t)}$, wobei der Umweg über die Spektralfunktionen gegangen werden soll.
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The output signal  ${y(t)} = {x(t)} ∗{h(t)}$  is sought, whereby the diversions via the spectral functions is to be taken.
  
  
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''Hinweis:''  
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*Die Aufgabe gehört zum  Kapitel  [[Signal_Representation/Faltungssatz_und_Faltungsoperation|Faltungssatz und Faltungsoperation]].
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''Hint:''  
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*This exercise belongs to the chapter  [[Signal_Representation/The_Convolution_Theorem_and_Operation|The Convolution Theorem and Operation]].
 
   
 
   
  
  
  
===Fragebogen===
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===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Geben Sie die Spektralfunktionen&nbsp; ${X(f)}$&nbsp; und&nbsp; ${H(f)}$&nbsp; an. Welche Werte ergeben sich für&nbsp; $f = 0$?
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{Give the spectral functions&nbsp; ${X(f)}$&nbsp; and&nbsp; ${H(f)}$&nbsp; an.&nbsp; Which values result for&nbsp; $f = 0$?
 
|type="{}"}
 
|type="{}"}
 
$X(f = 0)\ = \ $ { 4 3% } &nbsp;$\text{mV/Hz}$
 
$X(f = 0)\ = \ $ { 4 3% } &nbsp;$\text{mV/Hz}$
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{Berechnen Sie die Spektralfunktion&nbsp; ${Y(f)}$&nbsp; des Ausgangssignals. Wie groß ist der Spektralwert bei&nbsp; $f = 0$?
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{Calculate the spectral function&nbsp; ${Y(f)}$&nbsp; of the output signal.&nbsp; What is the spectral value at&nbsp; $f = 0$?
 
|type="{}"}
 
|type="{}"}
 
$Y(f = 0)\ = \ $ { 4 3% } &nbsp;$\text{mV/Hz}$
 
$Y(f = 0)\ = \ $ { 4 3% } &nbsp;$\text{mV/Hz}$
  
  
{Berechnen Sie den Ausgangsimpuls&nbsp; ${y(t)}$. Welche Werte ergeben sich für die Amplitude&nbsp; $y_0 = y(t = 0)$&nbsp; und die äquivalente Impulsdauer&nbsp; $\Delta t_y$?
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{Calculate the output pulse&nbsp; ${y(t)}$.&nbsp; What values result for the amplitude&nbsp; $y_0 = y(t = 0)$&nbsp; and the equivalent pulse duration&nbsp; $\Delta t_y$?
 
|type="{}"}
 
|type="{}"}
 
$y_0\ = \ $ { 0.8 3% } &nbsp;$\text{V}$
 
$y_0\ = \ $ { 0.8 3% } &nbsp;$\text{V}$
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</quiz>
 
</quiz>
  
===Musterlösung===
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===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp;  Durch Fouriertransformation erhält man:
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'''(1)'''&nbsp;  By Fourier transformation one obtains:
 
:$$X( f ) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } , \hspace{0.5cm}H(f) = {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_h  \hspace{0.05cm}\cdot \hspace{0.05cm}f} \right)^2 } .$$
 
:$$X( f ) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } , \hspace{0.5cm}H(f) = {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_h  \hspace{0.05cm}\cdot \hspace{0.05cm}f} \right)^2 } .$$
*Die gesuchten Werte sind
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*The values we are looking for are
:$$X(f = 0)\;\underline{ = 4 \,\text{mV/Hz}},$$
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:$$X(f = 0)\;\underline{ = 4 \,\text{mV/Hz}}, \hspace{0.5cm}
:$$H(f = 0)\; \underline{= 1}.$$
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H(f = 0)\; \underline{= 1}.$$
  
  
[[File:P_ID589__Sig_Z_3_9_b_neu.png|right|frame|Faltungsergebnis für &bdquo;$\rm Gauß  \ast  Gauß$&rdquo;]]
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[[File:P_ID589__Sig_Z_3_9_b_neu.png|right|frame|Gaussian spektra&nbsp; $X(f)$, &nbsp; &nbsp; $Y(f)$ &nbsp; &nbsp; &ndash; &nbsp; &nbsp; Gaussian pulses&nbsp; $x(t)$, &nbsp; &nbsp; $y(t)$]]
'''(2)'''&nbsp;  Der Faltung im Zeitbereich entspricht die Multiplikation im Frequenzbereich:
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'''(2)'''&nbsp;  Convolution in time domain corresponds to multiplication in frequency domain:
 
:$$Y(f) = X(f) \cdot H(f) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x^2  + \Delta t_h^2 } \right)f^2 } .$$
 
:$$Y(f) = X(f) \cdot H(f) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x^2  + \Delta t_h^2 } \right)f^2 } .$$
*Mit der Abkürzung&nbsp; $\Delta t_y = (\Delta t_x^2 + \Delta t_h^2)^{1/2} = 5\, \text{ms}$&nbsp; kann man hierfür schreiben:
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*With the abbreviation&nbsp; $\Delta t_y = (\Delta t_x^2 + \Delta t_h^2)^{1/2} = 5\, \text{ms}$&nbsp; one can write for this:
 
:$$Y(f) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } .$$
 
:$$Y(f) = x_0  \cdot \Delta t_x  \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } .$$
*Bei der Frequenz&nbsp; $f = 0$&nbsp; sind die Spektralwerte am Eingang und Ausgang des Gaußfilters gleich, also gilt:
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*At frequency&nbsp; $f = 0$&nbsp;, the spectral values at the input and output of the Gaussian filter are equal, so:
 
:$$Y(f = 0) \;\underline{= 4 \text{ mV/Hz}}.$$  
 
:$$Y(f = 0) \;\underline{= 4 \text{ mV/Hz}}.$$  
*Der Funktionsverlauf von&nbsp; ${Y(f)}$&nbsp; ist schmaler als&nbsp; ${X(f)}$&nbsp; und schmaler als&nbsp; ${H(f)}$.
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*The function curve of&nbsp; ${Y(f)}$&nbsp; is narrower than&nbsp; ${X(f)}$&nbsp; and narrower than&nbsp; ${H(f)}$.
  
  
  
'''(3)'''&nbsp;  Es gilt die folgende Fourierkorrespondenz:
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'''(3)'''&nbsp;  The following Fourier correspondence holds:
 
:$${\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 }\bullet\!\!\!-\!\!\!-\!\!\!-\!\!\circ\, \frac{1}{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
 
:$${\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y  \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 }\bullet\!\!\!-\!\!\!-\!\!\!-\!\!\circ\, \frac{1}{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
*Damit erhält man:
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*This gives:
 
:$$y(t) = x(t) * h(t) = x_0  \cdot \frac{\Delta t_x }{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
 
:$$y(t) = x(t) * h(t) = x_0  \cdot \frac{\Delta t_x }{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
*Der Maximalwert des Signals&nbsp; ${y(t)}$&nbsp; liegt ebenfalls bei&nbsp; $t = 0$&nbsp; und beträgt&nbsp; $y_0 \hspace{0.15cm}\underline{= 0.8 \text{ V} }$.  
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*The maximum value of the signal&nbsp; ${y(t)}$&nbsp; is also at &nbsp; $t = 0$&nbsp; and is &nbsp; $y_0 \hspace{0.15cm}\underline{= 0.8 \text{ V} }$.  
*Die äquivalente Impulsdauer ergibt sich zu&nbsp; $\Delta t_y \hspace{0.15cm}\underline{= 5 \text{ ms}}$&nbsp; (siehe obiges Bild, rechte Skizze).  
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*The equivalent pulse duration results in&nbsp; $\Delta t_y \hspace{0.15cm}\underline{= 5 \text{ ms}}$&nbsp; (see above graphic, right sketch).  
*Das bedeutet:&nbsp; Das Gaußfilter&nbsp; ${H(f)}$&nbsp; bewirkt, dass der Ausgangsimpuls&nbsp; ${y(t)}$&nbsp; kleiner und breiter als der Eingangsimpuls&nbsp; ${x(t)}$&nbsp; ist.  
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*This means:&nbsp; The Gaussian&nbsp; ${H(f)}$&nbsp; causes the output pulse&nbsp; ${y(t)}$&nbsp; to be smaller and wider than the input pulse&nbsp; ${x(t)}$&nbsp;.
*Die Impulsform bleibt weiterhin gaußförmig, weil: &nbsp; '''Gauß gefaltet mit Gauß ergibt immer Gauß!'''
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*The pulse shape remains Gaussian. &nbsp; Because: &nbsp; '''Gaussian convoluted with Gaussian always results in Gaussian!'''
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
 
__NOEDITSECTION__
 
__NOEDITSECTION__
[[Category:Exercises for Signal Representation|^3. Aperiodische Signale - Impulse^]]
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[[Category:Signal Representation: Exercises|^3.4 The Convolution Theorem^]]

Latest revision as of 15:45, 29 April 2021

Gaussian pulses  $x(t)$,   $h(t)$

The convolution result of two Gaussian functions is to be determined.  We consider

  • a Gaussian input pulse  ${x(t)}$  with amplitude $x_0 = 1\,\text{V}$ and  "equivalent pulse duration"  $\Delta t_x = 4 \,\text{ms}$,  as well as
  • a likewise Gaussian impulse response  ${h(t)}$, which has the  "equivalent pulse duration"  $\Delta t_h = 3 \,\text{ms}$ :
$$x( t ) = x_0 \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_x } )^2 } ,$$
$$h( t ) = \frac{1}{\Delta t_h } \cdot {\rm{e}}^{ - {\rm{\pi }}( {t/\Delta t_h } )^2 } .$$

The output signal  ${y(t)} = {x(t)} ∗{h(t)}$  is sought, whereby the diversions via the spectral functions is to be taken.




Hint:



Questions

1

Give the spectral functions  ${X(f)}$  and  ${H(f)}$  an.  Which values result for  $f = 0$?

$X(f = 0)\ = \ $

 $\text{mV/Hz}$
$H(f = 0)\ = \ $

2

Calculate the spectral function  ${Y(f)}$  of the output signal.  What is the spectral value at  $f = 0$?

$Y(f = 0)\ = \ $

 $\text{mV/Hz}$

3

Calculate the output pulse  ${y(t)}$.  What values result for the amplitude  $y_0 = y(t = 0)$  and the equivalent pulse duration  $\Delta t_y$?

$y_0\ = \ $

 $\text{V}$
$\Delta t_y\ = \ $

 $\text{ms}$


Solution

(1)  By Fourier transformation one obtains:

$$X( f ) = x_0 \cdot \Delta t_x \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } , \hspace{0.5cm}H(f) = {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_h \hspace{0.05cm}\cdot \hspace{0.05cm}f} \right)^2 } .$$
  • The values we are looking for are
$$X(f = 0)\;\underline{ = 4 \,\text{mV/Hz}}, \hspace{0.5cm} H(f = 0)\; \underline{= 1}.$$


Gaussian spektra  $X(f)$,     $Y(f)$     –     Gaussian pulses  $x(t)$,     $y(t)$

(2)  Convolution in time domain corresponds to multiplication in frequency domain:

$$Y(f) = X(f) \cdot H(f) = x_0 \cdot \Delta t_x \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_x^2 + \Delta t_h^2 } \right)f^2 } .$$
  • With the abbreviation  $\Delta t_y = (\Delta t_x^2 + \Delta t_h^2)^{1/2} = 5\, \text{ms}$  one can write for this:
$$Y(f) = x_0 \cdot \Delta t_x \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 } .$$
  • At frequency  $f = 0$ , the spectral values at the input and output of the Gaussian filter are equal, so:
$$Y(f = 0) \;\underline{= 4 \text{ mV/Hz}}.$$
  • The function curve of  ${Y(f)}$  is narrower than  ${X(f)}$  and narrower than  ${H(f)}$.


(3)  The following Fourier correspondence holds:

$${\rm{e}}^{ - {\rm{\pi }}\left( {\Delta t_y \hspace{0.05cm}\cdot \hspace{0.05cm} f} \right)^2 }\bullet\!\!\!-\!\!\!-\!\!\!-\!\!\circ\, \frac{1}{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
  • This gives:
$$y(t) = x(t) * h(t) = x_0 \cdot \frac{\Delta t_x }{\Delta t_y } \cdot {\rm{e}}^{ - {\rm{\pi }}\left( {t/\Delta t_y } \right)^2 } .$$
  • The maximum value of the signal  ${y(t)}$  is also at   $t = 0$  and is   $y_0 \hspace{0.15cm}\underline{= 0.8 \text{ V} }$.
  • The equivalent pulse duration results in  $\Delta t_y \hspace{0.15cm}\underline{= 5 \text{ ms}}$  (see above graphic, right sketch).
  • This means:  The Gaussian  ${H(f)}$  causes the output pulse  ${y(t)}$  to be smaller and wider than the input pulse  ${x(t)}$ .
  • The pulse shape remains Gaussian.   Because:   Gaussian convoluted with Gaussian always results in Gaussian!