Public Member Functions
BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit Class Reference

Class for linear analytic measurementmodels with additive gaussian noise. More...

#include <linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h>

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List of all members.

Public Member Functions

virtual void Calculate (const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &z, const MatrixWrapper::Matrix &R)=0
virtual
MatrixWrapper::SymmetricMatrix & 
CovarianceGet ()=0
 Returns covariance of the noise on the linearised measurement model evaluated using measurements z and states x.
virtual
MatrixWrapper::SymmetricMatrix 
CovarianceGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)=0
 Returns covariance of the noise on the linearised measurement model evaluated using current z and states x.
virtual MatrixWrapper::Matrix df_dxGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)=0
 Returns H-matrix calculated with measurement z and state x.
virtual MatrixWrapper::Matrix & df_dzGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)=0
 Returns D-matrix calculated with measurement z and state x.
virtual MatrixWrapper::Matrix & dfGet (int number)=0
virtual MatrixWrapper::ColumnVector ExpectedValueGet ()=0
 Return a prediction for the mean of the noise on the linear measurement equation, using the current x and z.
virtual const
MatrixWrapper::ColumnVector & 
fGet () const =0
virtual const int & Is_Identity () const =0
 Returns 1 if D-matrix equals the identity matrix else 0.
 LinearAnalyticMeasurementModelGaussianUncertainty_Implicit (LinearAnalyticConditionalGaussian *pdf)
 Constructor.
 LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ()
 Constructor.
virtual MatrixWrapper::ColumnVector PredictionGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)=0
 Return a prediction for the mean of the noise on the linear measurement equation, calculated with measurements z and state x.
virtual const
MatrixWrapper::Matrix & 
SRCovariance () const =0
 Returns square root of the covariance of the measurements z.
virtual const int TypeGet () const =0
virtual ~LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ()
 Destructor.

Detailed Description

Class for linear analytic measurementmodels with additive gaussian noise.

This class represents all measurement models of the form

\[ 0 = f (x,z) \]

as a linear measurement model with virtual measurement z_k^{virtual}

\[ z_k^{virtual} = H(x_k,z_k) \times x_k + N(\mu(x_{k},z_k) ,\Sigma(x_k,z_k)) \]

Definition at line 37 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h.


Constructor & Destructor Documentation

Constructor.

Parameters:
pdfConditional pdf, with Gaussian uncertainty

Definition at line 27 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.cpp.

Constructor.

Definition at line 32 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.cpp.


Member Function Documentation

virtual void BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::Calculate ( const MatrixWrapper::ColumnVector &  x,
const MatrixWrapper::ColumnVector &  z,
const MatrixWrapper::Matrix &  R 
) [pure virtual]
virtual MatrixWrapper::SymmetricMatrix& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::CovarianceGet ( ) [pure virtual]

Returns covariance of the noise on the linearised measurement model evaluated using measurements z and states x.

The linearised measurement equation look like:

\[ z_k^{virtual} = H(x_{k},z_k) \times x_k + N(\mu(x_{k},z_k) ,\Sigma(x_k,z_k)) \]

with noise

\[ =N(\mu(x_{k},z_k), \Sigma(x_k,z_k))\]

and covariance

\[ \Sigma(x_k,z_k)= D(x_k,z_k)*R*D(x_k,z_k)' \]

and R the noise on the measurements z .

virtual MatrixWrapper::SymmetricMatrix BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::CovarianceGet ( const MatrixWrapper::ColumnVector &  z,
const MatrixWrapper::ColumnVector &  x 
) [pure virtual]

Returns covariance of the noise on the linearised measurement model evaluated using current z and states x.

The linearised measurement equation look like:

\[ z_k^{virtual} = H(x_{k},z_k) \times x_k + N(\mu(x_{k},z_k) ,\Sigma(x_k,z_k)) \]

with noise

\[ =N(\mu(x_{k},z_k), \Sigma(x_k,z_k))\]

and covariance

\[ \Sigma(x_k,z_k)= D(x_k,z_k)*R*D(x_k,z_k)' \]

and R the noise on the measurements z .

Reimplemented from BFL::LinearAnalyticMeasurementModelGaussianUncertainty.

virtual MatrixWrapper::Matrix BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::df_dxGet ( const MatrixWrapper::ColumnVector &  z,
const MatrixWrapper::ColumnVector &  x 
) [pure virtual]

Returns H-matrix calculated with measurement z and state x.

\[ H = \frac{df}{dx} \mid_{ z, x} \]

used to determine the covariance of noise on the linear measurement equation

Parameters:
zThe value of the input in which the derivate is evaluated
xThe value in the state in which the derivate is evaluated

Reimplemented from BFL::LinearAnalyticMeasurementModelGaussianUncertainty.

virtual MatrixWrapper::Matrix& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::df_dzGet ( const MatrixWrapper::ColumnVector &  z,
const MatrixWrapper::ColumnVector &  x 
) [pure virtual]

Returns D-matrix calculated with measurement z and state x.

\[ D = \frac{df}{dz} \mid_{ z, x} \]

used to determine the covariance of noise on the linear measurement equation

Parameters:
zThe value of the input in which the derivate is evaluated
xThe value in the state in which the derivate is evaluated
virtual MatrixWrapper::Matrix& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::dfGet ( int  number) [pure virtual]
virtual MatrixWrapper::ColumnVector BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::ExpectedValueGet ( ) [pure virtual]

Return a prediction for the mean of the noise on the linear measurement equation, using the current x and z.

virtual const MatrixWrapper::ColumnVector& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::fGet ( ) const [pure virtual]

Returns 1 if D-matrix equals the identity matrix else 0.

virtual MatrixWrapper::ColumnVector BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::PredictionGet ( const MatrixWrapper::ColumnVector &  z,
const MatrixWrapper::ColumnVector &  x 
) [pure virtual]

Return a prediction for the mean of the noise on the linear measurement equation, calculated with measurements z and state x.

Reimplemented from BFL::LinearAnalyticMeasurementModelGaussianUncertainty.

virtual const MatrixWrapper::Matrix& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::SRCovariance ( ) const [pure virtual]

Returns square root of the covariance of the measurements z.


The documentation for this class was generated from the following files:


bfl
Author(s): Klaas Gadeyne, Wim Meeussen, Tinne Delaet and many others. See web page for a full contributor list. ROS package maintained by Wim Meeussen.
autogenerated on Thu Feb 11 2016 22:31:57