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

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[[File:P_ID671__Sto_A_4_16.png|right|frame|Three correlation matrices]]
 
[[File:P_ID671__Sto_A_4_16.png|right|frame|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.
+
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 2D–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.
 
 
 
 
 
  
 +
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.
  
  
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Hints:
 
Hints:
 
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
 
*The exercise belongs to the chapter  [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables|Generalization to N-Dimensional Random Variables]].
*Some basics on the application of vectors and matrices can be found on the pages  [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]]  and  [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]] .  
+
*Some basics on the application of vectors and matrices can be found on the pages   [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Determinant_of_a_matrix|Determinant of a Matrix]]   and   [[Theory_of_Stochastic_Signals/Generalization_to_N-Dimensional_Random_Variables#Basics_of_matrix_operations:_Inverse_of_a_matrix|Inverse of a Matrix]] .  
* According to the page  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables#Contour_lines_for_correlated_random_variables|Contour lines for correlated random variables]]  the angle $\alpha$ between the old and the new system is given by the following equation:
+
* According to the page  [[Theory_of_Stochastic_Signals/Two-Dimensional_Gaussian_Random_Variables#Contour_lines_for_correlated_random_variables|"Contour lines for correlated random variables"]]  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
 
:$$\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}).$$  
*In particular, note:  
+
*In particular,  note:  
 
**A  $2×2$-covariance matrix has two real eigenvalues  $\lambda_1$  and  $\lambda_2$.  
 
**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 two eigenvalues determine two eigenvectors  $\xi_1$  and  $\xi_2$.  
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|type="[]"}
 
|type="[]"}
 
+ $\mathbf{K_y}$  describes all possible two-dimensional random variables with  $\sigma_1 = \sigma_2 = \sigma$.
 
+ $\mathbf{K_y}$  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  $-1 \le \rho \le +1$.
- The value range of the parameter &nbsp;$\rho$&nbsp; is &nbsp;$0 < \rho < 1$.
+
- The value range of the parameter &nbsp;$\rho$ &nbsp; is &nbsp;$0 < \rho < 1$.
  
  
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{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 $, assuming &nbsp;$\lambda_1 \ge \lambda_2$&nbsp;?
+
{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 \ = \ $ { 1.5 3% } $\ (\lambda_1 \ge \lambda_2)$
 
$\lambda_1 \ = \ $ { 1.5 3% } $\ (\lambda_1 \ge \lambda_2)$
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{Calculate the corresponding eigenvectors&nbsp; $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$.&nbsp; Which of the following statements are true?
 
{Calculate the corresponding eigenvectors&nbsp; $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$.&nbsp; Which of the following statements are true?
 
|type="[]"}
 
|type="[]"}
+ $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$&nbsp; lie in the direction of the ellipse principal axes.
+
+ $\mathbf{\eta_1}$ &nbsp;and&nbsp; $\mathbf{\eta_2}$&nbsp; lie in the direction of the ellipse main axes.
+ The new coordinates are rotated by&nbsp; $45^\circ$&nbsp;.
+
+ The new coordinates are rotated by&nbsp; $45^\circ$.
- The scatterings with respect to the new system are&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$.
+
- The standard deviations with respect to the new system are&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$.
  
  
{What are the characteristics of the random variable specified by&nbsp; $\mathbf{K_z}$&nbsp; $\mathbf{z}$?
+
{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% }
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===Solution===
 
===Solution===
 
{{ML-Kopf}}
 
{{ML-Kopf}}
'''(1)'''&nbsp; Correct are <u>proposed solutions 1 and 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 2D random variable with&nbsp; $\sigma_1 = \sigma_2 = \sigma$.  
+
*$\mathbf{K_y}$&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.
 
*The parameter&nbsp; $\rho$&nbsp; specifies the correlation coefficient.&nbsp; This can take all values between&nbsp; $\pm 1$&nbsp; including these marginal values.
 
   
 
   
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'''(2)'''&nbsp; In this case, the governing equation is:
 
'''(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 \\
 
0 & 1- \lambda
 
0 & 1- \lambda
 
\end{array} \right] = 0 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}
 
\end{array} \right] = 0 \hspace{0.3cm}\Rightarrow \hspace{0.3cm}
(1- \lambda)^2 = 0\hspace{0.3cm}\Rightarrow
+
(1- \lambda)^2 = 0\hspace{0.3cm}\Rightarrow
 
\hspace{0.3cm} \hspace{0.15cm}\underline{\lambda_{1/2} =1}.$$
 
\hspace{0.3cm} \hspace{0.15cm}\underline{\lambda_{1/2} =1}.$$
  
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*For&nbsp; $\rho= 0.5$&nbsp; one gets&nbsp; $\underline{\lambda_{1} =1.5}$&nbsp; and&nbsp; $\underline{\lambda_{2} =0.5}$.  
 
*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, the equation holds in the whole domain of definition&nbsp; $-1 \le \rho \le +1$.  
+
*By the way,&nbsp; the equation holds in the whole domain of definition&nbsp; $-1 \le \rho \le +1$.  
*For&nbsp; $\rho = 0$&nbsp; is&nbsp; $\lambda_1 = \lambda_2 = +1$&nbsp; &nbsp; &rArr; &nbsp; see subtask&nbsp; '''(2)''''.  
+
*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; this gives $\lambda_1 = 2$&nbsp; and&nbsp; $\lambda_2 = 0$.
+
*For&nbsp; $\rho = \pm 1$ &nbsp; &rArr; &nbsp; $\lambda_1 = 2$&nbsp; and&nbsp; $\lambda_2 = 0$.
  
  
  
'''(4)'''&nbsp; The eigenvectors are obtained by substituting the eigenvalues&nbsp; $\lambda_1$&nbsp; and&nbsp; $\lambda_2$&nbsp; into the correlation matrix:
+
'''(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 \\
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\end{array} \right].$$
 
\end{array} \right].$$
  
[[File:P_ID676__Sto_A_4_16_d.png|right|frame|To rotate the coordinate system]]
+
[[File:P_ID676__Sto_A_4_16_d.png|right|frame|Rotate the coordinate system]]
Putting this into what is called orthonormal form, the following holds:
+
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}
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The sketch illustrates the result:  
 
The sketch illustrates the result:  
*The coordinate system defined by&nbsp; $\mathbf{\eta_1}$&nbsp; and&nbsp; $\mathbf{\eta_2}$&nbsp; is actually in the direction of the principal axes of the original system.  
+
*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 angle of rotation&nbsp; $\alpha = 45^\circ$.  
+
*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:
 
*This also follows from the equation given in the theory section:
 
:$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot
 
:$$\alpha = {1}/{2}\cdot \arctan (2 \cdot\rho \cdot
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{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 scatter with respect to the new axes, but the variances.  
+
*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.  
 
 
  
Thus, correct are <u>the proposed solutions 1 and 2</u>.
 
  
  
'''(5)'''&nbsp; By comparing the matrices&nbsp; $\mathbf{K_x}$&nbsp; and&nbsp; $\mathbf{K_z}$&nbsp; we get.  
+
'''(5)'''&nbsp; By comparing the matrices &nbsp; $\mathbf{K_x}$ &nbsp; and &nbsp; $\mathbf{K_z}$ &nbsp; we get.  
 
*$\sigma_{1}\hspace{0.15cm}\underline{ =2}$,
 
*$\sigma_{1}\hspace{0.15cm}\underline{ =2}$,
 
*$\sigma_{2}\hspace{0.15cm}\underline{ =1}$,
 
*$\sigma_{2}\hspace{0.15cm}\underline{ =1}$,
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The accompanying sketch shows the joint PDF of the random variable&nbsp; $\mathbf{z}$:
 
The accompanying sketch shows the joint PDF of the random variable&nbsp; $\mathbf{z}$:
*Because&nbsp; $\rho = 1$&nbsp; all values lie on the correlation line with coordinates&nbsp; $z_1$&nbsp; and&nbsp; $z_2 = z_1/2$.  
+
*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.  
+
*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; $\mathbf{\zeta_1}$ is&nbsp; $\lambda_1 = 5$&nbsp; $($scatter&nbsp; $\sigma_1 = \sqrt{5} = 2.236)$,  
+
*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; $\mathbf{\zeta_2}$&nbsp; the random variable is not extended&nbsp; $(\lambda_2 = \sigma_2 = 0)$.
+
*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}}
  

Latest revision as of 15:43, 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  $(\lambda_2 = \sigma_2 = 0)$.