Difference between revisions of "Aufgaben:Exercise 5.2Z: DFT of a Triangular Pulse"
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− | [[File:P_ID1140__Sig_Z_5_2.png|right|frame| | + | [[File:P_ID1140__Sig_Z_5_2.png|right|frame|Discretisation of a triangular pulse]] |
− | + | Consider the sketched triangular pulse | |
:$$x(t) = \left\{ \begin{array}{l} A \cdot \left( 1 - {|t|}/{T} \right ) \\ | :$$x(t) = \left\{ \begin{array}{l} A \cdot \left( 1 - {|t|}/{T} \right ) \\ | ||
\hspace{0.25cm} 0 \\ \end{array} \right.\quad | \hspace{0.25cm} 0 \\ \end{array} \right.\quad | ||
− | \begin{array}{*{10}c} {\rm{ | + | \begin{array}{*{10}c} {\rm{for}} |
− | \\ {\rm{ | + | \\ {\rm{for}} \\ \end{array}\begin{array}{*{10}c} |
{\left| \hspace{0.005cm} t\hspace{0.05cm} \right| \le T,} \\ | {\left| \hspace{0.005cm} t\hspace{0.05cm} \right| \le T,} \\ | ||
{\left|\hspace{0.005cm} t \hspace{0.05cm} \right| > T.} \\ | {\left|\hspace{0.005cm} t \hspace{0.05cm} \right| > T.} \\ | ||
\end{array}$$ | \end{array}$$ | ||
− | + | The signal parameters have the following values: | |
− | * | + | * amplitude A=4 V, |
− | * | + | * equivalent pulse duration Δt=T=1ms. |
− | + | The spectrum X(f) is obtained by applying [[Signal_Representation/Fourier_Transform_and_its_Inverse#The_first_Fourier_integral|the first Fourier Integral]]: | |
− | :X(f)=A⋅T⋅si2(πfT). | + | :$$X(f) = A \cdot T \cdot {\rm si}^2(\pi f T)\hspace{0.5cm}\text{with}\hspace{0.5cm}{\rm si}(x)=\sin(x)/x\hspace{0.05cm}.$$ |
− | + | The spectral function is now to be approximated by a Discrete Fourier Transform $\rm (DFT)$ with N=8 , where the N coefficients for the time domain ⇒ d(0), ... , d(7) can be taken from the graph. | |
− | + | The corresponding spectral coefficients D(0), ... , D(7) are to be determined. For the indices \mu = 0, ... , N–1 applies: | |
:$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} | :$$D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} | ||
d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$ | d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.$$ | ||
− | + | If we denote the distance between two samples in the time domain by T_{\rm A} and the corresponding frequency distance between two lines by f_{\rm A}, following relationship applies: | |
:N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.05cm}. | :N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.05cm}. | ||
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− | '' | + | ''Hints:'' |
− | * | + | *This task belongs to the chapter [[Signal_Representation/Discrete_Fourier_Transform_(DFT)|Discrete Fourier Transformation (DFT)]]. |
− | * | + | *The topic dealt with here is also dealt with in the interactive applet [[Applets:Discrete_Fouriertransform_and_Inverse|Discrete Fourier Transform and Inverse]]. |
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− | === | + | ===Question=== |
<quiz display=simple> | <quiz display=simple> | ||
− | { | + | {Give the time coefficients. What are the values of d(0), d(3) and d(6)? |
|type="{}"} | |type="{}"} | ||
d(0)\ = \ { 4 3% } \text{V} | d(0)\ = \ { 4 3% } \text{V} | ||
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− | { | + | {What is the distance T_{\rm A} between two time samples? |
|type="{}"} | |type="{}"} | ||
T_{\rm A}\ = \ { 0.25 3% } \text{ms} | T_{\rm A}\ = \ { 0.25 3% } \text{ms} | ||
− | { | + | {What is the distance f_{\rm A} between two DFT frequency samples? |
|type="{}"} | |type="{}"} | ||
f_{\rm A}\ = \ { 0.5 3% } \text{kHz} | f_{\rm A}\ = \ { 0.5 3% } \text{kHz} | ||
− | { | + | {Calculate the coefficient D(0) and interpret the result. |
|type="{}"} | |type="{}"} | ||
D(0)\ = \ { 2 3% } \text{V} | D(0)\ = \ { 2 3% } \text{V} | ||
− | { | + | {Calculate the coefficient D(2) and interpret the result, also in terms of the coefficients D(4) and D(6). |
|type="{}"} | |type="{}"} | ||
D(2)\ = \ { 0. } \text{V} | D(2)\ = \ { 0. } \text{V} | ||
− | { | + | {Calculate and interpret the DFT coefficient D(7). |
|type="{}"} | |type="{}"} | ||
D(7)\ = \ { 0.854 3% } \text{V} | D(7)\ = \ { 0.854 3% } \text{V} | ||
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</quiz> | </quiz> | ||
− | === | + | ===Solution=== |
{{ML-Kopf}} | {{ML-Kopf}} | ||
− | '''(1)''' | + | '''(1)''' From the graph the following values result with A = 4 \ {\rm V} : |
:$${d(0) = 4\,{\rm V}, \hspace{0.1cm}d(1) = d(7) = 3\,{\rm V}, \hspace{0.1cm} | :$${d(0) = 4\,{\rm V}, \hspace{0.1cm}d(1) = d(7) = 3\,{\rm V}, \hspace{0.1cm} | ||
\hspace{0.1cm}d(2) = d(6) = 2\,{\rm V}, \hspace{0.1cm} | \hspace{0.1cm}d(2) = d(6) = 2\,{\rm V}, \hspace{0.1cm} | ||
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− | '''(2)''' | + | '''(2)''' According to the diagram T_{\rm A} = T/4. |
− | * | + | *With T = 1 \ \text{ms} one obtains \underline{T_{\rm A} = 0.25 \ \text{ms}}. |
− | '''(3)''' | + | '''(3)''' For the distances of the samples in the time and frequency domain applies: |
:$$N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}f_{\rm | :$$N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}f_{\rm | ||
A}= \frac{1}{ 8 \cdot 0.25\, {\rm ms}}\hspace{0.15 cm}\underline{= 0.5\, {\rm kHz}}\hspace{0.05cm}.$$ | A}= \frac{1}{ 8 \cdot 0.25\, {\rm ms}}\hspace{0.15 cm}\underline{= 0.5\, {\rm kHz}}\hspace{0.05cm}.$$ | ||
− | '''(4)''' | + | '''(4)''' With N = 8 and \mu = 0 , it follows from the DFT equation: |
:$$D(0) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | :$$D(0) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | ||
d(\nu) = \frac{1\,{\rm V}}{8}\cdot (4+3+2+1+0+1+2+3)\hspace{0.15 cm}\underline{= 2 \,{\rm V}}\hspace{0.05cm}.$$ | d(\nu) = \frac{1\,{\rm V}}{8}\cdot (4+3+2+1+0+1+2+3)\hspace{0.15 cm}\underline{= 2 \,{\rm V}}\hspace{0.05cm}.$$ | ||
− | * | + | *The DFT value D(0) describes the spectral value at f = 0, where the following relation holds: |
:X(f=0) = \frac{D(0)}{f_{\rm A}}= \frac{ 2\,{\rm V}}{0.5\,{\rm kHz}}= 4 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}. | :X(f=0) = \frac{D(0)}{f_{\rm A}}= \frac{ 2\,{\rm V}}{0.5\,{\rm kHz}}= 4 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}. | ||
− | * | + | *This value agrees with the theoretical value (A \cdot T) . |
− | '''(5)''' | + | '''(5)''' With N = 8 and \mu = 2 we obtain: |
:$$D(2) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | :$$D(2) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | ||
d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = | d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = | ||
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d(\nu)\cdot (-{\rm j})^{\nu} \hspace{0.3cm} | d(\nu)\cdot (-{\rm j})^{\nu} \hspace{0.3cm} | ||
\Rightarrow \hspace{0.3cm} = \frac{1\,{\rm V}}{8}\cdot (4-3\cdot{\rm j}-2+{\rm j}-{\rm j}-2+3\cdot{\rm j})\hspace{0.15 cm}\underline{= 0}\hspace{0.05cm}.$$ | \Rightarrow \hspace{0.3cm} = \frac{1\,{\rm V}}{8}\cdot (4-3\cdot{\rm j}-2+{\rm j}-{\rm j}-2+3\cdot{\rm j})\hspace{0.15 cm}\underline{= 0}\hspace{0.05cm}.$$ | ||
− | + | This result could have been predicted without calculation: | |
− | * | + | *The DFT coefficients D(\mu) are at the same time the Fourier coefficients of the function $x_{\rm Per}(t)$ periodised at the distance $T_{\rm P} = 2T$. <br>This is shown as a dashed line in the graph on the information page. |
− | * | + | *Due to symmetry properties, however, all even Fourier coefficients of the function x_{\rm Per}(t) are equal to zero ⇒ D(4)\hspace{0.15cm}\underline{=0}, D(6)\hspace{0.15cm}\underline{=0}. |
− | '''(6)''' | + | '''(6)''' The coefficient D(7) describes the periodised spectral function at the frequency f = 7 \cdot f_{\rm A}. Due to periodicity and symmetry property holds: |
:D(7) = D(-1) = D^{\star}(1) \hspace{0.05cm}. | :D(7) = D(-1) = D^{\star}(1) \hspace{0.05cm}. | ||
− | + | Preferably, we calculate this DFT coefficient: | |
:$$D(1) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | :$$D(1) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} | ||
d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = | d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = | ||
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\frac{-1 + {\rm j}}{\sqrt{2}}-{\rm j}+ 2\cdot {\rm j}+3\cdot \frac{1 - {\rm j}}{\sqrt{2}}\right)$$ | \frac{-1 + {\rm j}}{\sqrt{2}}-{\rm j}+ 2\cdot {\rm j}+3\cdot \frac{1 - {\rm j}}{\sqrt{2}}\right)$$ | ||
:\Rightarrow \; \; D(1) = \frac{2 + \sqrt{2}}{4} \approx 0.854{\rm V}\hspace{0.05cm}. | :\Rightarrow \; \; D(1) = \frac{2 + \sqrt{2}}{4} \approx 0.854{\rm V}\hspace{0.05cm}. | ||
− | + | Since D(1) is purely real, D(7) = D(1) \; \underline{= 0.854 \ {\rm V}}. | |
− | + | This gives for the corresponding values of the continuous spectral function: | |
:X(f=-f_{\rm A}) = X(f=+f_{\rm A}) =\frac{D(1)}{f_{\rm A}}= 1.708 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}. | :X(f=-f_{\rm A}) = X(f=+f_{\rm A}) =\frac{D(1)}{f_{\rm A}}= 1.708 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}. | ||
− | * | + | *Because of the implicit periodic continuation by the DFT, the value calculated in this way does not exactly match the actual value (4 \cdot A \cdot T/\pi^2 = 1.621 · 10^{-3}\text{ V/Hz}). |
− | * | + | *The relative error is approx. 5.3\%. |
{{ML-Fuß}} | {{ML-Fuß}} | ||
__NOEDITSECTION__ | __NOEDITSECTION__ | ||
− | [[Category: | + | [[Category:Signal Representation: Exercises|^5.2 Discrete Fourier Transform^]] |
Latest revision as of 17:59, 16 May 2021
Consider the sketched triangular pulse
- x(t) = \left\{ \begin{array}{l} A \cdot \left( 1 - {|t|}/{T} \right ) \\ \hspace{0.25cm} 0 \\ \end{array} \right.\quad \begin{array}{*{10}c} {\rm{for}} \\ {\rm{for}} \\ \end{array}\begin{array}{*{10}c} {\left| \hspace{0.005cm} t\hspace{0.05cm} \right| \le T,} \\ {\left|\hspace{0.005cm} t \hspace{0.05cm} \right| > T.} \\ \end{array}
The signal parameters have the following values:
- amplitude A = 4 \ \text{V},
- equivalent pulse duration \Delta t = T = 1 \, \text{ms}.
The spectrum X(f) is obtained by applying the first Fourier Integral:
- X(f) = A \cdot T \cdot {\rm si}^2(\pi f T)\hspace{0.5cm}\text{with}\hspace{0.5cm}{\rm si}(x)=\sin(x)/x\hspace{0.05cm}.
The spectral function is now to be approximated by a Discrete Fourier Transform \rm (DFT) with N = 8 , where the N coefficients for the time domain ⇒ d(0), ... , d(7) can be taken from the graph.
The corresponding spectral coefficients D(0), ... , D(7) are to be determined. For the indices \mu = 0, ... , N–1 applies:
- D(\mu) = \frac{1}{N} \cdot \sum_{\nu = 0 }^{N-1} d(\nu)\cdot {w}^{\hspace{0.05cm}\nu \hspace{0.05cm} \cdot \hspace{0.05cm}\mu} \hspace{0.05cm}.
If we denote the distance between two samples in the time domain by T_{\rm A} and the corresponding frequency distance between two lines by f_{\rm A}, following relationship applies:
- N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.05cm}.
Hints:
- This task belongs to the chapter Discrete Fourier Transformation (DFT).
- The topic dealt with here is also dealt with in the interactive applet Discrete Fourier Transform and Inverse.
Question
Solution
- {d(0) = 4\,{\rm V}, \hspace{0.1cm}d(1) = d(7) = 3\,{\rm V}, \hspace{0.1cm} \hspace{0.1cm}d(2) = d(6) = 2\,{\rm V}, \hspace{0.1cm} \hspace{0.1cm}d(3) = d(5) = 1\,{\rm V}, \hspace{0.1cm} \hspace{0.1cm}d(4) = 0}\hspace{0.05cm}.
- \Rightarrow \hspace{0.15 cm}\underline{d(0) = 4\,{\rm V}, \hspace{0.1cm}d(3) = 1\,{\rm V}, \hspace{0.1cm}d(6) = 2\,{\rm V}. \hspace{0.1cm}} \hspace{0.05cm}
(2) According to the diagram T_{\rm A} = T/4.
- With T = 1 \ \text{ms} one obtains \underline{T_{\rm A} = 0.25 \ \text{ms}}.
(3) For the distances of the samples in the time and frequency domain applies:
- N \cdot f_{\rm A} \cdot T_{\rm A} = 1 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}f_{\rm A}= \frac{1}{ 8 \cdot 0.25\, {\rm ms}}\hspace{0.15 cm}\underline{= 0.5\, {\rm kHz}}\hspace{0.05cm}.
(4) With N = 8 and \mu = 0 , it follows from the DFT equation:
- D(0) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} d(\nu) = \frac{1\,{\rm V}}{8}\cdot (4+3+2+1+0+1+2+3)\hspace{0.15 cm}\underline{= 2 \,{\rm V}}\hspace{0.05cm}.
- The DFT value D(0) describes the spectral value at f = 0, where the following relation holds:
- X(f=0) = \frac{D(0)}{f_{\rm A}}= \frac{ 2\,{\rm V}}{0.5\,{\rm kHz}}= 4 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}.
- This value agrees with the theoretical value (A \cdot T) .
(5) With N = 8 and \mu = 2 we obtain:
- D(2) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /2) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} d(\nu)\cdot (-{\rm j})^{\nu} \hspace{0.3cm} \Rightarrow \hspace{0.3cm} = \frac{1\,{\rm V}}{8}\cdot (4-3\cdot{\rm j}-2+{\rm j}-{\rm j}-2+3\cdot{\rm j})\hspace{0.15 cm}\underline{= 0}\hspace{0.05cm}.
This result could have been predicted without calculation:
- The DFT coefficients D(\mu) are at the same time the Fourier coefficients of the function x_{\rm Per}(t) periodised at the distance T_{\rm P} = 2T.
This is shown as a dashed line in the graph on the information page. - Due to symmetry properties, however, all even Fourier coefficients of the function x_{\rm Per}(t) are equal to zero ⇒ D(4)\hspace{0.15cm}\underline{=0}, D(6)\hspace{0.15cm}\underline{=0}.
(6) The coefficient D(7) describes the periodised spectral function at the frequency f = 7 \cdot f_{\rm A}. Due to periodicity and symmetry property holds:
- D(7) = D(-1) = D^{\star}(1) \hspace{0.05cm}.
Preferably, we calculate this DFT coefficient:
- D(1) = \frac{1}{8}\cdot \sum_{\nu = 0 }^{7} d(\nu)\cdot {\rm e}^{-{\rm j} \hspace{0.05cm}\cdot \hspace{0.05cm} (\pi /4) \hspace{0.05cm}\cdot \hspace{0.05cm} \nu} = \frac{1\,{\rm V}}{8}\cdot \left(4 +3\cdot \frac{1 - {\rm j}}{\sqrt{2}}-2\cdot {\rm j}+ \frac{-1 - {\rm j}}{\sqrt{2}}-{\rm j}+ \frac{-1 + {\rm j}}{\sqrt{2}}-{\rm j}+ 2\cdot {\rm j}+3\cdot \frac{1 - {\rm j}}{\sqrt{2}}\right)
- \Rightarrow \; \; D(1) = \frac{2 + \sqrt{2}}{4} \approx 0.854{\rm V}\hspace{0.05cm}.
Since D(1) is purely real, D(7) = D(1) \; \underline{= 0.854 \ {\rm V}}.
This gives for the corresponding values of the continuous spectral function:
- X(f=-f_{\rm A}) = X(f=+f_{\rm A}) =\frac{D(1)}{f_{\rm A}}= 1.708 \cdot 10^{-3}\,{\rm V/Hz}\hspace{0.05cm}.
- Because of the implicit periodic continuation by the DFT, the value calculated in this way does not exactly match the actual value (4 \cdot A \cdot T/\pi^2 = 1.621 · 10^{-3}\text{ V/Hz}).
- The relative error is approx. 5.3\%.