Difference between revisions of "Aufgaben:Exercise 4.16: Eigenvalues and Eigenvectors"

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{{quiz-Header|Buchseite=Stochastische Signaltheorie/Verallgemeinerung auf N-dimensionale Zufallsgrößen
+
{{quiz-Header|Buchseite=Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables
 
}}
 
}}
  
[[File:P_ID671__Sto_A_4_16.png|right|Drei Korrelationsmatrizen]]
+
[[File:P_ID671__Sto_A_4_16.png|right|frame|Three correlation matrices]]
Obwohl die Beschreibung Gaußscher Zufallsgrößen mit Hilfe von Vektoren und Matrizen eigentlich nur bei mehr als <i>N</i> = 2 Dimensionen erforderlich ist und Sinn macht, beschränken wir uns hier auf den Sonderfall zweidimensionaler Zufallsgrößen.
+
Although the description of Gaussian random variables using vectors and matrices is actually only necessary and makes sense for more than&nbsp; $N = 2$&nbsp; dimensions,&nbsp; here we restrict ourselves to the special case of two-dimensional random variables for simplicity.
  
:In der Grafik ist oben die allgemeine Korrelationsmatrix <b>K<sub>x</sub></b> der 2D&ndash;Zufallsgröße <b>x</b> = (<i>x</i><sub>1</sub>, <i>x</i><sub>2</sub>)<sup>T</sup> angegeben, wobei <i>&sigma;</i><sub>1</sub><sup>2</sup> und <i>&sigma;</i><sub>2</sub><sup>2</sup> die Varianzen der Einzelkomponenten beschreiben. <i>&rho;</i> bezeichnet den Korrelationskoeffizienten zwischen den beiden Komponenten.
+
In the graph above,&nbsp; the general correlation matrix&nbsp; $\mathbf{K_x}$&nbsp; of the two-dimensional random variable &nbsp; $\mathbf{x} = (x_1, x_2)^{\rm T}$ &nbsp; is given,&nbsp; where&nbsp; $\sigma_1^2$&nbsp; and&nbsp; $\sigma_2^2$&nbsp; describe the variances of the individual components. &nbsp; $\rho$&nbsp; denotes the correlation coefficient between the two components.
  
:Die Zufallsgrößen <b>y</b> und <b>z</b> geben zwei Spezialfälle von <b>x</b> an, deren Prozessparameter aus den Kovarianzmatrizen <b>K<sub>y</sub></b> und <b>K<sub>z</sub></b> bestimmt werden können.
+
The random variables &nbsp; $\mathbf{y}$ &nbsp; and &nbsp; $\mathbf{z}$ &nbsp; give two special cases of&nbsp; $\mathbf{x}$&nbsp; whose process parameters are to be determined from the correlation matrices &nbsp; $\mathbf{K_y}$ &nbsp; and &nbsp; $\mathbf{K_z}$ &nbsp; respectively.
  
  
''Hinweise:''
 
*Die Aufgabe gehört zum  Kapitel [[Stochastische_Signaltheorie/Verallgemeinerung_auf_N-dimensionale_Zufallsgrößen|Verallgemeinerung auf N-dimensionale Zufallsgrößen]].
 
*Einige Grundlagen zur Anwendung von Vektoren und Matrizen finden sich auf den Seiten [[Stochastische_Signaltheorie/Verallgemeinerung_auf_N-dimensionale_Zufallsgrößen#Grundlagen_der_Matrizenrechnung:_Determinante_einer_Matrix|Determinante einer Matrix]] sowie [[Stochastische_Signaltheorie/Verallgemeinerung_auf_N-dimensionale_Zufallsgrößen#Grundlagen_der_Matrizenrechnung:_Inverse_einer_Matrix|Inverse einer Matrix]]
 
*Sollte die Eingabe des Zahlenwertes &bdquo;0&rdquo; erforderlich sein, so geben Sie bitte &bdquo;0.&rdquo; ein.
 
  
:<b>Hinweis:</b> Die Aufgabe bezieht sich auf die theoretischen Grundlagen von Kapitel 4.7. Einige Grundlagen zur Anwendung von Vektoren und Matrizen finden sich auf den folgenden Seiten:<br> &nbsp;&nbsp;&nbsp;&nbsp; Determinante einer Matrix,<br> &nbsp;&nbsp;&nbsp;&nbsp; Inverse einer Matrix.
 
:<br><br>Weiterhin ist zu beachten:
 
  
:* Eine 2&times;2-Kovarianzmatrix besitzt zwei reelle Eigenwerte <i>&lambda;</i><sub>1</sub> und <i>&lambda;</i><sub>2</sub>.<br>
 
  
:* Die beiden Eigenwerte bestimmen zwei Eigenvektoren <i>&xi;</i><sub>1</sub> und <i>&xi;</i><sub>2</sub> und  diese spannen ein neues Koordinatensystem in Richtung der Hauptachsen des alten Systems auf.
 
  
:* Entsprechend der Seite Höhenlinien bei korrelierten Zufallsgrößen ist der Winkel <i>&alpha;</i> zwischen dem alten und dem neuen System durch folgende Gleichung gegeben:
+
Hints:
:$$\alpha = \frac{1}{2}\cdot \arctan (2 \cdot\rho \cdot
+
*The exercise belongs to the chapter &nbsp;[[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
\frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2}).$$
+
*Some basics on the application of vectors and matrices can be found on the pages &nbsp; [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]] &nbsp; and &nbsp; [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]]&nbsp;.
===Fragebogen===
+
* According to the page &nbsp;[[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables#Contour_lines_for_correlated_random_variables|"Contour lines for correlated random variables"]]&nbsp; the angle $\alpha$ between the old and the new system is given by the following equation:
 +
:$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot
 +
\frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2}).$$  
 +
*In particular,&nbsp; note:
 +
**A&nbsp; $2×2$-covariance matrix has two real eigenvalues&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$.
 +
**These two eigenvalues determine two eigenvectors&nbsp; $\xi_1$&nbsp; and&nbsp; $\xi_2$.
 +
**These span a new coordinate system in the direction of the principal axes of the old system.
 +
 
 +
 
 +
 
 +
===Questions===
  
 
<quiz display=simple>
 
<quiz display=simple>
{Welche Aussagen treffen für die Kovarianzmatrix <b>K<sub>y</sub></b> zu?
+
{Which statements are true for the correlation matrix&nbsp; $\mathbf{K_y}$&nbsp;?
 
|type="[]"}
 
|type="[]"}
+ <b>K<sub>y</sub></b> beschreibt alle möglichen 2D-Zufallsgrößen mit <i>&sigma;</i><sub>1</sub> = <i>&sigma;</i><sub>2</sub>.
+
+ $\mathbf{K_y}$&nbsp; describes all possible two-dimensional random variables with &nbsp;$\sigma_1 = \sigma_2 = \sigma$.
+ Der Wertebereich des Parameters <i>&rho;</i> ist &ndash;1 &#8804; <i>&rho;</i> &#8804; 1.
+
+ The value range of the parameter &nbsp;$\rho$ &nbsp; is &nbsp;$-1 \le \rho \le +1$.
- Der Wertebereich des Parameters <i>&rho;</i> ist 0 < <i>&rho;</i> < 1.
+
- The value range of the parameter &nbsp;$\rho$ &nbsp; is &nbsp;$0 < \rho < 1$.
  
  
{Berechnen Sie die Eigenwerte von <b>K<sub>y</sub></b> unter der Bedingung <i>&sigma;</i> = 1, <i>&rho;</i> = 0.
+
{Calculate the eigenvalues of&nbsp; $\mathbf{K_y}$&nbsp; under the condition &nbsp;$\sigma = 1$&nbsp; and &nbsp;$\rho = 0$.
 
|type="{}"}
 
|type="{}"}
$\lambda_1$ = { 1 3% }
+
$\lambda_1 \ = \ $ { 1 3% } $\ (\lambda_1 \ge \lambda_2)$
$\lambda_2$ = { 1 3% }
+
$\lambda_2 \ = \ $ { 1 3% } $\ (\lambda_2 \le \lambda_1)$
  
  
{Geben Sie die Eigenwerte von <b>K<sub>y</sub></b> unter der Bedingung <i>&sigma;</i> = 1, 0 < <i>&rho;</i> < 1 an. Welche Werte ergeben sich für <i>&rho;</i> = 0.5, wobei <i>&lambda;</i><sub>1</sub> &#8805; <i>&lambda;</i><sub>2</sub> vorausgesetzt wird?
+
{Give the eigenvalues of &nbsp; $\mathbf{K_y}$ &nbsp; under the condition &nbsp; $\sigma = 1$ &nbsp; and &nbsp; $0 < \rho < 1$ &nbsp; What values result for &nbsp;$\rho = 0.5 $,&nbsp; assuming &nbsp;$\lambda_1 \ge \lambda_2$?
 
|type="{}"}
 
|type="{}"}
$\lambda_1$ = { 2 3% }
+
$\lambda_1 \ = \ $ { 1.5 3% } $\ (\lambda_1 \ge \lambda_2)$
$\lambda_2$ = { 0 3% }
+
$\lambda_2 \ = \ $ { 0.5 3% } $\ (\lambda_2 \le \lambda_1)$
  
  
{Berechnen Sie die zugehörigen Eigenvektoren <b>&eta;<sub>1</sub></b> und <b>&eta;<sub>2</sub></b>. Welche der folgenden Aussagen sind zutreffend?
+
{Calculate the corresponding eigenvectors&nbsp; $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$.&nbsp; Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
+ <b>&eta;<sub>1</sub></b> und <b>&eta;<sub>2</sub></b> liegen in Richtung der Ellipsenhauptachsen.
+
+ $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$&nbsp; lie in the direction of the ellipse main axes.
+ Die neuen Koordinaten sind  um 45&deg; gedreht.
+
+ The new coordinates are rotated by&nbsp; $45^\circ$.
- Die Streuungen bezüglich des neuen Systems sind <i>&lambda;</i><sub>1</sub> und <i>&lambda;</i><sub>2</sub>.
+
- The standard deviations with respect to the new system are&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$.
  
  
{Wie lauten die Kenngrößen der durch <b>K<sub>z</sub></b> festgelegten Zufallsgröße <b>z</b>?
+
{What are the characteristics of the random variable&nbsp; $\mathbf{z}$&nbsp; specified by&nbsp; $\mathbf{K_z}$?
 
|type="{}"}
 
|type="{}"}
$\sigma_1$ = { 2 3% }
+
$\sigma_1 = \ $ { 2 3% }
$\sigma_2$ = { 1 3% }
+
$\sigma_2 = \ $ { 1 3% }
$\rho$ = { 2 3% }
+
$\rho = \ $ { 1 3% }
  
  
{Berechnen Sie die Eigenwerte <i>&lambda;</i><sub>1</sub> und
+
{Calculate the eigenvalues&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2 \le \lambda_1$&nbsp; of the correlation matrix&nbsp; $\mathbf{K_z}$.
<i>&lambda;</i><sub>2</sub> < <i>&lambda;</i><sub>1</sub> der Kovarianzmatrix <b>K<sub>z</sub></b>.
 
 
|type="{}"}
 
|type="{}"}
$\lambda_1$ = { 5 3% }
+
$\lambda_1 \ = \ $ { 5 3% } $\ (\lambda_1 \ge \lambda_2)$
$\lambda_2$ = { 0 3% }
+
$\lambda_2 \ = \ $ { 0. } $\ (\lambda_2 \le \lambda_1)$
  
  
{Um welchen Winkel <i>&alpha;</i> ist das neue Koordinatensystem (<b>&zeta;<sub>1</sub></b>, <b>&zeta;<sub>2</sub></b>) gegenüber dem ursprünglichen System (<b>z<sub>1</sub></b>, <b>z<sub>2</sub></b>) gedreht?
+
{By what angle&nbsp; $\alpha$&nbsp; is the new coordinate system&nbsp; $(\mathbf{\zeta_1}, \ \mathbf{\zeta_2})$&nbsp; rotated with respect to the original system&nbsp; $(\mathbf{z_1}, \ \mathbf{z_2})$&nbsp;?
 
|type="{}"}
 
|type="{}"}
$\alpha$ = { 26.56 3% } Grad
+
$\alpha \ = \ $ { 26.56 3% } $\ \rm deg$
  
  
Line 77: Line 78:
 
</quiz>
 
</quiz>
  
===Musterlösung===
+
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
:<b>1.</b>&nbsp;&nbsp;<b>K<sub>y</sub></b> ist tatsächlich die allgemeinste Kovariationmatrix einer 2D-Zufallsgröße mit <i>&sigma;</i><sub>1</sub> = <i>&sigma;</i><sub>2</sub> = <i>&sigma;</i>. Der zweite Parameter gibt den Korrelationskoeffizienten an. Nach Abschnitt 4.1 kann <i>&rho;</i> alle Werte zwischen &plusmn;1 inclusive dieser Randwerte annehmen. Richtig sind <u>die Lösungsvorschläge 1 und 2</u>.
+
'''(1)'''&nbsp; Correct are the&nbsp; <u>proposed solutions 1 and 2</u>:
 +
*$\mathbf{K_y}$&nbsp; is indeed the most general correlation matrix of a two-dimensional random variable with&nbsp; $\sigma_1 = \sigma_2 = \sigma$.
 +
*The parameter&nbsp; $\rho$&nbsp; specifies the correlation coefficient.&nbsp; This can take all values between&nbsp; $\pm 1$&nbsp; including these marginal values.
 +
  
:<b>2.</b>)&nbsp;&nbsp;In diesem Fall lautet die Bestimmungsgleichung:
+
 
 +
'''(2)'''&nbsp; In this case, the governing equation is:
 
:$${\rm det}\left[ \begin{array}{cc}
 
:$${\rm det}\left[ \begin{array}{cc}
 
1- \lambda & 0 \\
 
1- \lambda & 0 \\
Line 89: Line 94:
 
\hspace{0.3cm} \hspace{0.15cm}\underline{\lambda_{1/2} =1}.$$
 
\hspace{0.3cm} \hspace{0.15cm}\underline{\lambda_{1/2} =1}.$$
  
:<b>3.</b>&nbsp;&nbsp; Bei positivem <i>&rho;</i> lautet die Bestimmungsgleichung der Eigenwerte:
 
:$$(1- \lambda)^2 -\rho^2 = 0\hspace{0.3cm}\Rightarrow
 
\hspace{0.3cm}\lambda^2 - 2\lambda + 1 - \rho^2 =
 
0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\lambda_{1/2} =1 \pm \rho.$$
 
  
:Für <i>&rho;</i> = 0.5 erhält man <i>&lambda;</i><sub>1</sub> <u>= 1.5</u> und <i>&lambda;</i><sub>2</sub> <u>= 0.5</u>. Die Gleichung gilt übrigens im gesamten Definitionsbereich &ndash;1 &#8804; <i>&rho;</i> &#8804; 1. Für <i>&rho;</i> = 0 ist <i>&lambda;</i><sub>1</sub> = <i>&lambda;</i><sub>2</sub> = 1 (siehe Teilaufgabe 2). Bei <i>&rho;</i> = &plusmn;1 ergibt sich <i>&lambda;</i><sub>1</sub> = 2 und <i>&lambda;</i><sub>2</sub> = 0.
 
  
:<b>4.</b>&nbsp;&nbsp;Die Eigenvektoren erhält man durch Einsetzen der Eigenwerte <i>&lambda;</i><sub>1</sub>, <i>&lambda;</i><sub>2</sub> in die Kovarianzmatrix:
+
'''(3)'''&nbsp; With positive&nbsp; $\rho$&nbsp; the governing equation of the eigenvalues is:
 +
:$$(1- \lambda)^2 -\rho^2 = 0\hspace{0.5cm}\Rightarrow
 +
\hspace{0.5cm}\lambda^2 - 2\lambda + 1 - \rho^2 =
 +
0\hspace{0.5cm}\Rightarrow\hspace{0.5cm}\lambda_{1/2} =1 \pm \rho.$$
 +
 
 +
*For&nbsp; $\rho= 0.5$&nbsp; one gets&nbsp; $\underline{\lambda_{1} =1.5}$&nbsp; and&nbsp; $\underline{\lambda_{2} =0.5}$.
 +
*By the way,&nbsp; the equation holds in the whole domain of definition&nbsp; $-1 \le \rho \le +1$.
 +
*For&nbsp; $\rho = 0$ &nbsp; &rArr; &nbsp; $\lambda_1 = \lambda_2 = +1$&nbsp; &nbsp; &rArr; &nbsp; see subtask&nbsp; '''(2)'''.
 +
*For&nbsp; $\rho = \pm 1$ &nbsp; &rArr; &nbsp; $\lambda_1 = 2$&nbsp; and&nbsp; $\lambda_2 = 0$.
 +
 
 +
 
 +
 
 +
'''(4)'''&nbsp; Correct are&nbsp; <u>the proposed solutions 1 and 2</u>.
 +
 
 +
The eigenvectors are obtained by substituting the eigenvalues&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$&nbsp; into the correlation matrix:
 
:$$\left[ \begin{array}{cc}
 
:$$\left[ \begin{array}{cc}
 
1- (1+\rho) & \rho \\
 
1- (1+\rho) & \rho \\
Line 133: Line 147:
 
1
 
1
 
\end{array} \right].$$
 
\end{array} \right].$$
[[File:P_ID676__Sto_A_4_16_d.png|right|]]
 
  
:Bringt man diese in die so genannte Orthonormalform, so gilt:
+
[[File:P_ID676__Sto_A_4_16_d.png|right|frame|Rotate the coordinate system]]
 +
Putting this into the&nbsp; "orthonormal form",&nbsp; the following holds:
 
:$${\boldsymbol{\eta_1}}= \frac{1}{\sqrt{2}}\cdot \left[
 
:$${\boldsymbol{\eta_1}}= \frac{1}{\sqrt{2}}\cdot \left[
 
\begin{array}{c}
 
\begin{array}{c}
Line 147: Line 161:
 
\end{array} \right].$$
 
\end{array} \right].$$
  
:In nebenstehender Skizze ist das Ergebnis verdeutlicht. Das neue, durch <b>&eta;<sub>1</sub></b> und <b>&eta;<sub>2</sub></b> festgelegte Koordinatensystem liegt tatsächlich in Richtung der Hauptachsen des ursprünglichen Systems. Mit <i>&sigma;</i><sub>1</sub> = <i>&sigma;</i><sub>2</sub> ergibt sich stets (Ausnahme: <i>&rho;</i> = 0) der Drehwinkel <i>&alpha;</i> = 45 Grad. Dies folgt auch aus der Gleichung auf Seite 3 von Kapitel 4.2:
+
The sketch illustrates the result:  
:$$\alpha = \frac{1}{2}\cdot \arctan (2 \cdot\rho \cdot
+
*The coordinate system defined by&nbsp; $\mathbf{\eta_1}$&nbsp; and&nbsp; $\mathbf{\eta_2}$&nbsp; is actually in the direction of the main axes of the original system.  
 +
*With&nbsp; $\sigma_1 = \sigma_2$&nbsp; almost always results&nbsp; $($exception: &nbsp; $\rho= 0)$&nbsp; the rotation angle&nbsp; $\alpha = 45^\circ$.  
 +
*This also follows from the equation given in the theory section:
 +
:$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot
 
\frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2})=
 
\frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2})=
\frac{1}{2}\cdot \arctan
+
{1}/{2}\cdot \arctan
(\infty)\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\alpha = 45^\circ.$$
+
(\infty)\hspace{0.3cm}\rightarrow\hspace{0.3cm}\alpha = 45^\circ.$$
 +
*The eigenvalues&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$&nbsp; do not denote the standard deviations with respect to the new axes, but the variances.
 +
 
  
:Die Eigenwerte <i>&lambda;</i><sub>1</sub> und <i>&lambda;</i><sub>2</sub> kennzeichnen nicht die Streuungen bezüglich der neuen Achsen, sondern die entsprechenden Varianzen. Richtig sind <u>die Lösungsvorschläge 1 und 2</u>.
 
  
:<b>5.</b>&nbsp;&nbsp;Durch Vergleich der Matrizen <b>K<sub>x</sub></b> und <b>K<sub>z</sub></b> erhält man <u><i>&sigma;</i><sub>1</sub> = 2, <i>&sigma;</i><sub>2</sub> = 1 und <i>&rho;</i> = 1</u>.
+
'''(5)'''&nbsp; By comparing the matrices &nbsp; $\mathbf{K_x}$ &nbsp; and &nbsp; $\mathbf{K_z}$ &nbsp; we get.
 +
*$\sigma_{1}\hspace{0.15cm}\underline{ =2}$,
 +
*$\sigma_{2}\hspace{0.15cm}\underline{ =1}$,
 +
*$\rho = 2/(\sigma_{1} \cdot \sigma_{2})\hspace{0.15cm}\underline{ =1}$.
  
:<b>6.</b>&nbsp;&nbsp;Nach dem inzwischen altbekannten Schema gilt:
+
 
 +
 
 +
'''(6)'''&nbsp; According to the now familiar scheme:
 
:$$(4- \lambda) \cdot (1- \lambda) -4 = 0\hspace{0.3cm}\Rightarrow
 
:$$(4- \lambda) \cdot (1- \lambda) -4 = 0\hspace{0.3cm}\Rightarrow
\hspace{0.3cm}\lambda^2 - 5\lambda =
+
\hspace{0.3cm}\lambda^2 - 5\lambda =
 
0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\hspace{0.15cm}\underline{\lambda_{1}
 
0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\hspace{0.15cm}\underline{\lambda_{1}
 
=5,\hspace{0.1cm} \lambda_{2} =0}.$$
 
=5,\hspace{0.1cm} \lambda_{2} =0}.$$
  
:<b>7.</b>&nbsp;&nbsp;Nach der auf dem Angabenblatt vorgegebenen Gleichung gilt:
+
 
:$$\alpha = \frac{1}{2}\cdot \arctan (2 \cdot 1 \cdot \frac{2 \cdot
+
 
1}{2^2 -1^2})= \frac{1}{2}\cdot \arctan (\frac{4}{3}) =
+
'''(7)'''&nbsp; According to the equation given on the specification sheet:
 +
:$$\alpha ={1}/{2}\cdot \arctan (2 \cdot 1 \cdot \frac{2 \cdot
 +
1}{2^2 -1^2})= {1}/{2}\cdot \arctan ({4}/{3}) =
 
26.56^\circ.$$
 
26.56^\circ.$$
[[File:P_ID677__Sto_A_4_16_g.png|right|]]
 
  
:Zum gleichen Ergebnis gelangt man über den Eigenvektor:
+
[[File:P_ID677__Sto_A_4_16_g.png|right|frame|Best possible decorrelation]]
 +
The same result is obtained using the eigenvector:
 
:$$\left[ \begin{array}{cc}
 
:$$\left[ \begin{array}{cc}
 
4-5 & 2 \\
 
4-5 & 2 \\
Line 177: Line 202:
 
\zeta_{12}
 
\zeta_{12}
 
\end{array}
 
\end{array}
\right]=0$$
+
\right]=0 \hspace{0.3cm}
:$$\Rightarrow\hspace{0.3cm}-\zeta_{11}=
+
\Rightarrow\hspace{0.3cm}-\zeta_{11}=
2\zeta_{12}=0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\zeta_{12}=\frac{\zeta_{11}}{2}$$
+
2\zeta_{12}=0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\zeta_{12}={\zeta_{11}}/{2}$$
 
:$$\Rightarrow\hspace{0.3cm}\alpha = \arctan
 
:$$\Rightarrow\hspace{0.3cm}\alpha = \arctan
(\frac{\zeta_{12}}{\zeta_{11}}) = \arctan(0.5) \hspace{0.15cm}\underline{= 26.56^\circ}.$$
+
({\zeta_{12}}/{\zeta_{11}}) = \arctan(0.5) \hspace{0.15cm}\underline{= 26.56^\circ}.$$
  
:Die nebenstehende Skizze zeigt die 2D-WDF der Zufallsgröße <b><i>z</i></b>. Wegen <i>&rho;</i> = 1 liegen alle Werte auf der Korrelationsgeraden mit den Koordinaten <i>z</i><sub>2</sub> = <i>z</i><sub>1</sub>/2. Durch die Drehung um den Winkel <i>&alpha;</i> = arctan(0.5) = 26.56 Grad entsteht ein neues Koordinatensystem. Die Varianz entlang der Achse <i>&zeta;</i><sub>1</sub> beträgt <i>&lambda;</i><sub>1</sub> = 5 (Streuung <i>&sigma;</i><sub>1</sub> = 2.236), während in der dazu orthogonalen Richtung <i>&zeta;</i><sub>2</sub> die Zufallsgröße nicht ausgedehnt ist (<i>&lambda;</i><sub>2</sub> = <i>&sigma;</i><sub>2</sub> = 0).
+
The accompanying sketch shows the joint PDF of the random variable&nbsp; $\mathbf{z}$:
 +
*Because of&nbsp; $\rho = 1$&nbsp; all values lie on the correlation line with coordinates&nbsp; $z_1$&nbsp; and&nbsp; $z_2 = z_1/2$.  
 +
*By rotating by the angle &nbsp; $\alpha = \arctan(0.5) = 26.56^\circ$ &nbsp; a new coordinate system is formed.  
 +
*The variance along the axis &nbsp; $\mathbf{\zeta_1}$ &nbsp; is&nbsp; $\lambda_1 = 5$&nbsp; $($standard deviation&nbsp; $\sigma_1 = \sqrt{5} = 2.236)$,  
 +
*while in the direction orthogonal to it,&nbsp; the random variable&nbsp; $\mathbf{\zeta_2}$&nbsp; is not extended&nbsp; $(\lambda_2 = \sigma_2 = 0)$.
 
{{ML-Fuß}}
 
{{ML-Fuß}}
  
  
  
[[Category:Aufgaben zu Stochastische Signaltheorie|^4.7 N-dimensionale Zufallsgrößen^]]
+
[[Category:Theory of Stochastic Signals: Exercises|^4.7 N-dimensionale Zufallsgrößen^]]

Latest revision as of 14:43, 29 March 2022

Three correlation matrices

Although the description of Gaussian random variables using vectors and matrices is actually only necessary and makes sense for more than  $N = 2$  dimensions,  here we restrict ourselves to the special case of two-dimensional random variables for simplicity.

In the graph above,  the general correlation matrix  $\mathbf{K_x}$  of the two-dimensional random variable   $\mathbf{x} = (x_1, x_2)^{\rm T}$   is given,  where  $\sigma_1^2$  and  $\sigma_2^2$  describe the variances of the individual components.   $\rho$  denotes the correlation coefficient between the two components.

The random variables   $\mathbf{y}$   and   $\mathbf{z}$   give two special cases of  $\mathbf{x}$  whose process parameters are to be determined from the correlation matrices   $\mathbf{K_y}$   and   $\mathbf{K_z}$   respectively.




Hints:

$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot \frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2}).$$
  • In particular,  note:
    • A  $2×2$-covariance matrix has two real eigenvalues  $\lambda_1$  and  $\lambda_2$.
    • These two eigenvalues determine two eigenvectors  $\xi_1$  and  $\xi_2$.
    • These span a new coordinate system in the direction of the principal axes of the old system.


Questions

1

Which statements are true for the correlation matrix  $\mathbf{K_y}$ ?

$\mathbf{K_y}$  describes all possible two-dimensional random variables with  $\sigma_1 = \sigma_2 = \sigma$.
The value range of the parameter  $\rho$   is  $-1 \le \rho \le +1$.
The value range of the parameter  $\rho$   is  $0 < \rho < 1$.

2

Calculate the eigenvalues of  $\mathbf{K_y}$  under the condition  $\sigma = 1$  and  $\rho = 0$.

$\lambda_1 \ = \ $

$\ (\lambda_1 \ge \lambda_2)$
$\lambda_2 \ = \ $

$\ (\lambda_2 \le \lambda_1)$

3

Give the eigenvalues of   $\mathbf{K_y}$   under the condition   $\sigma = 1$   and   $0 < \rho < 1$   What values result for  $\rho = 0.5 $,  assuming  $\lambda_1 \ge \lambda_2$?

$\lambda_1 \ = \ $

$\ (\lambda_1 \ge \lambda_2)$
$\lambda_2 \ = \ $

$\ (\lambda_2 \le \lambda_1)$

4

Calculate the corresponding eigenvectors  $\mathbf{\eta_1}$  and  $\mathbf{\eta_2}$.  Which of the following statements are true?

$\mathbf{\eta_1}$  and  $\mathbf{\eta_2}$  lie in the direction of the ellipse main axes.
The new coordinates are rotated by  $45^\circ$.
The standard deviations with respect to the new system are  $\lambda_1$  and  $\lambda_2$.

5

What are the characteristics of the random variable  $\mathbf{z}$  specified by  $\mathbf{K_z}$?

$\sigma_1 = \ $

$\sigma_2 = \ $

$\rho = \ $

6

Calculate the eigenvalues  $\lambda_1$  and  $\lambda_2 \le \lambda_1$  of the correlation matrix  $\mathbf{K_z}$.

$\lambda_1 \ = \ $

$\ (\lambda_1 \ge \lambda_2)$
$\lambda_2 \ = \ $

$\ (\lambda_2 \le \lambda_1)$

7

By what angle  $\alpha$  is the new coordinate system  $(\mathbf{\zeta_1}, \ \mathbf{\zeta_2})$  rotated with respect to the original system  $(\mathbf{z_1}, \ \mathbf{z_2})$ ?

$\alpha \ = \ $

$\ \rm deg$


Solution

(1)  Correct are the  proposed solutions 1 and 2:

  • $\mathbf{K_y}$  is indeed the most general correlation matrix of a two-dimensional random variable with  $\sigma_1 = \sigma_2 = \sigma$.
  • The parameter  $\rho$  specifies the correlation coefficient.  This can take all values between  $\pm 1$  including these marginal values.


(2)  In this case, the governing equation is:

$${\rm det}\left[ \begin{array}{cc} 1- \lambda & 0 \\ 0 & 1- \lambda \end{array} \right] = 0 \hspace{0.3cm}\Rightarrow \hspace{0.3cm} (1- \lambda)^2 = 0\hspace{0.3cm}\Rightarrow \hspace{0.3cm} \hspace{0.15cm}\underline{\lambda_{1/2} =1}.$$


(3)  With positive  $\rho$  the governing equation of the eigenvalues is:

$$(1- \lambda)^2 -\rho^2 = 0\hspace{0.5cm}\Rightarrow \hspace{0.5cm}\lambda^2 - 2\lambda + 1 - \rho^2 = 0\hspace{0.5cm}\Rightarrow\hspace{0.5cm}\lambda_{1/2} =1 \pm \rho.$$
  • For  $\rho= 0.5$  one gets  $\underline{\lambda_{1} =1.5}$  and  $\underline{\lambda_{2} =0.5}$.
  • By the way,  the equation holds in the whole domain of definition  $-1 \le \rho \le +1$.
  • For  $\rho = 0$   ⇒   $\lambda_1 = \lambda_2 = +1$    ⇒   see subtask  (2).
  • For  $\rho = \pm 1$   ⇒   $\lambda_1 = 2$  and  $\lambda_2 = 0$.


(4)  Correct are  the proposed solutions 1 and 2.

The eigenvectors are obtained by substituting the eigenvalues  $\lambda_1$  and  $\lambda_2$  into the correlation matrix:

$$\left[ \begin{array}{cc} 1- (1+\rho) & \rho \\ \rho & 1- (1+\rho) \end{array} \right]\cdot{\boldsymbol{\eta_1}} = \left[ \begin{array}{cc} -\rho & \rho \\ \rho & -\rho \end{array} \right]\cdot \left[ \begin{array}{c} \eta_{11} \\ \eta_{12} \end{array} \right]=0$$
$$\Rightarrow\hspace{0.3cm}-\rho \cdot \eta_{11} + \rho \cdot \eta_{12} = 0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\eta_{11}= {\rm const} \cdot \eta_{12}\hspace{0.3cm}\Rightarrow\hspace{0.3cm}{\boldsymbol{\eta_1}}= {\rm const}\cdot \left[ \begin{array}{c} 1 \\ 1 \end{array} \right];$$
$$\left[ \begin{array}{cc} 1- (1-\rho) & \rho \\ \rho & 1- (1-\rho) \end{array} \right]\cdot{\boldsymbol{\eta_2}} = \left[ \begin{array}{cc} \rho & \rho \\ \rho & \rho \end{array} \right]\cdot \left[ \begin{array}{c} \eta_{21} \\ \eta_{22} \end{array} \right]=0$$
$$\Rightarrow\hspace{0.3cm}\rho \cdot \eta_{21} + \rho \cdot \eta_{22} = 0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\eta_{21}= -{\rm const} \cdot \eta_{22}\hspace{0.3cm}\Rightarrow\hspace{0.3cm}{\boldsymbol{\eta_2}}= {\rm const}\cdot \left[ \begin{array}{c} -1 \\ 1 \end{array} \right].$$
Rotate the coordinate system

Putting this into the  "orthonormal form",  the following holds:

$${\boldsymbol{\eta_1}}= \frac{1}{\sqrt{2}}\cdot \left[ \begin{array}{c} 1 \\ 1 \end{array} \right],\hspace{0.5cm} {\boldsymbol{\eta_2}}= \frac{1}{\sqrt{2}}\cdot \left[ \begin{array}{c} -1 \\ 1 \end{array} \right].$$

The sketch illustrates the result:

  • The coordinate system defined by  $\mathbf{\eta_1}$  and  $\mathbf{\eta_2}$  is actually in the direction of the main axes of the original system.
  • With  $\sigma_1 = \sigma_2$  almost always results  $($exception:   $\rho= 0)$  the rotation angle  $\alpha = 45^\circ$.
  • This also follows from the equation given in the theory section:
$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot \frac{\sigma_1\cdot\sigma_2}{\sigma_1^2 -\sigma_2^2})= {1}/{2}\cdot \arctan (\infty)\hspace{0.3cm}\rightarrow\hspace{0.3cm}\alpha = 45^\circ.$$
  • The eigenvalues  $\lambda_1$  and  $\lambda_2$  do not denote the standard deviations with respect to the new axes, but the variances.


(5)  By comparing the matrices   $\mathbf{K_x}$   and   $\mathbf{K_z}$   we get.

  • $\sigma_{1}\hspace{0.15cm}\underline{ =2}$,
  • $\sigma_{2}\hspace{0.15cm}\underline{ =1}$,
  • $\rho = 2/(\sigma_{1} \cdot \sigma_{2})\hspace{0.15cm}\underline{ =1}$.


(6)  According to the now familiar scheme:

$$(4- \lambda) \cdot (1- \lambda) -4 = 0\hspace{0.3cm}\Rightarrow \hspace{0.3cm}\lambda^2 - 5\lambda = 0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\hspace{0.15cm}\underline{\lambda_{1} =5,\hspace{0.1cm} \lambda_{2} =0}.$$


(7)  According to the equation given on the specification sheet:

$$\alpha ={1}/{2}\cdot \arctan (2 \cdot 1 \cdot \frac{2 \cdot 1}{2^2 -1^2})= {1}/{2}\cdot \arctan ({4}/{3}) = 26.56^\circ.$$
Best possible decorrelation

The same result is obtained using the eigenvector:

$$\left[ \begin{array}{cc} 4-5 & 2 \\ 2 & 1-5 \end{array} \right]\cdot \left[ \begin{array}{c} \zeta_{11} \\ \zeta_{12} \end{array} \right]=0 \hspace{0.3cm} \Rightarrow\hspace{0.3cm}-\zeta_{11}= 2\zeta_{12}=0\hspace{0.3cm}\Rightarrow\hspace{0.3cm}\zeta_{12}={\zeta_{11}}/{2}$$
$$\Rightarrow\hspace{0.3cm}\alpha = \arctan ({\zeta_{12}}/{\zeta_{11}}) = \arctan(0.5) \hspace{0.15cm}\underline{= 26.56^\circ}.$$

The accompanying sketch shows the joint PDF of the random variable  $\mathbf{z}$:

  • Because of  $\rho = 1$  all values lie on the correlation line with coordinates  $z_1$  and  $z_2 = z_1/2$.
  • By rotating by the angle   $\alpha = \arctan(0.5) = 26.56^\circ$   a new coordinate system is formed.
  • The variance along the axis   $\mathbf{\zeta_1}$   is  $\lambda_1 = 5$  $($standard deviation  $\sigma_1 = \sqrt{5} = 2.236)$,
  • while in the direction orthogonal to it,  the random variable  $\mathbf{\zeta_2}$  is not extended  $(\lambda_2 = \sigma_2 = 0)$.