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

#include <linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h>

Inheritance diagram for BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit:

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

BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ( LinearAnalyticConditionalGaussian * pdf )

Constructor.

Parameters:

pdf	Conditional pdf, with Gaussian uncertainty

Definition at line 27 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.cpp.

BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ( )

Constructor.

Definition at line 32 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.cpp.

BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::~LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ( ) [virtual]

Destructor.

Definition at line 43 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:

z	The value of the input in which the derivate is evaluated
x	The 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:

z	The value of the input in which the derivate is evaluated
x	The 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]

virtual const int& BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::Is_Identity ( ) 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.

virtual const int BFL::LinearAnalyticMeasurementModelGaussianUncertainty_Implicit::TypeGet ( ) const [pure virtual]

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

Public Member Functions

Detailed Description

Constructor & Destructor Documentation

Member Function Documentation