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

#include <linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h>

Inheritance diagram for LinearAnalyticMeasurementModelGaussianUncertainty_Implicit:

Public Member Functions
	LinearAnalyticMeasurementModelGaussianUncertainty_Implicit (LinearAnalyticConditionalGaussian *pdf)
	Constructor.

	LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ()
	Constructor.

virtual	~LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ()
	Destructor.

virtual const MatrixWrapper::ColumnVector &	fGet () const =0

virtual const int	TypeGet () const =0

virtual MatrixWrapper::Matrix &	dfGet (int number)=0

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::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 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 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 void	Calculate (const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &z, const MatrixWrapper::Matrix &R)=0

virtual const MatrixWrapper::Matrix &	SRCovariance () const =0
	Returns square root of the covariance of the measurements z.

virtual const int &	Is_Identity () const =0
	Returns 1 if D-matrix equals the identity matrix else 0.

void	HSet (const MatrixWrapper::Matrix &h)
	Set Matrix H.

void	JSet (const MatrixWrapper::Matrix &j)
	Set Matrix J.

const MatrixWrapper::Matrix &	HGet () const
	Get Matrix H.

const MatrixWrapper::Matrix &	JGet () const
	Get Matrix J.

int	MeasurementSizeGet () const
	Get Measurement Size.

bool	SystemWithoutSensorParams () const
	Number of Conditional Arguments.

ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > *	MeasurementPdfGet ()
	Get the MeasurementPDF.

void	MeasurementPdfSet (ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > *pdf)
	Set the MeasurementPDF.

MatrixWrapper::ColumnVector	Simulate (const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &s, const SampleMthd sampling_method=SampleMthd::DEFAULT, void *sampling_args=NULL)
	Simulate the Measurement, given a certain state, and an input.

MatrixWrapper::ColumnVector	Simulate (const MatrixWrapper::ColumnVector &x, const SampleMthd sampling_method=SampleMthd::DEFAULT, void *sampling_args=NULL)
	Simulate the system (no input system)

Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &s)
	Get the probability of a certain measurement.

Probability	ProbabilityGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)
	Get the probability of a certain measurement.

Protected Attributes
ConditionalPdf< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > *	_MeasurementPdf
	ConditionalPdf representing $P(Z_k \| X_{k}, U_{k})$ .

bool	_systemWithoutSensorParams
	System with no sensor params??

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

◆ LinearAnalyticMeasurementModelGaussianUncertainty_Implicit()

LinearAnalyticMeasurementModelGaussianUncertainty_Implicit ( LinearAnalyticConditionalGaussian * pdf )

Constructor.

Parameters

pdf	Conditional pdf, with Gaussian uncertainty

Member Function Documentation

◆ CovarianceGet() [1/2]

virtual MatrixWrapper::SymmetricMatrix & 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 .

◆ CovarianceGet() [2/2]

virtual MatrixWrapper::SymmetricMatrix 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 LinearAnalyticMeasurementModelGaussianUncertainty.

◆ df_dxGet()

virtual MatrixWrapper::Matrix 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 LinearAnalyticMeasurementModelGaussianUncertainty.

◆ df_dzGet()

virtual MatrixWrapper::Matrix & 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

◆ HSet()

void HSet ( const MatrixWrapper::Matrix & h )

inherited

Set Matrix H.

This can be particularly useful for time-varying systems

Parameters

h Matrix H

◆ JSet()

void JSet ( const MatrixWrapper::Matrix & j )

inherited

Set Matrix J.

This can be particularly useful for time-varying systems

Parameters

j Matrix J

◆ MeasurementPdfSet()

void MeasurementPdfSet ( ConditionalPdf< MatrixWrapper::ColumnVector , MatrixWrapper::ColumnVector > * pdf )

inherited

Set the MeasurementPDF.

Parameters

pdf	a pointer to the measurement pdf

◆ PredictionGet()

virtual MatrixWrapper::ColumnVector 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 LinearAnalyticMeasurementModelGaussianUncertainty.

◆ ProbabilityGet() [1/2]

Probability ProbabilityGet	(	const MatrixWrapper::ColumnVector &	z,
		const MatrixWrapper::ColumnVector &	x
	)

inherited

Get the probability of a certain measurement.

(measurement independent of input) gived a certain state and input

Parameters

z	the measurement value
x	x current state of the system

Returns: the "probability" of the measurement

◆ ProbabilityGet() [2/2]

Probability ProbabilityGet	(	const MatrixWrapper::ColumnVector &	z,
		const MatrixWrapper::ColumnVector &	x,
		const MatrixWrapper::ColumnVector &	s
	)

inherited

Get the probability of a certain measurement.

given a certain state and input

Parameters

z	the measurement value
x	current state of the system
s	the sensor param value

Returns: the "probability" of the measurement

◆ Simulate() [1/2]

MatrixWrapper::ColumnVector Simulate	(	const MatrixWrapper::ColumnVector &	x,
		const MatrixWrapper::ColumnVector &	s,
		const SampleMthd	sampling_method = `SampleMthd::DEFAULT`,
		void *	sampling_args = `NULL`
	)

inherited

Simulate the Measurement, given a certain state, and an input.

Parameters

x	current state of the system
s	sensor parameter

Returns: Measurement generated by simulating the measurement model

Parameters

sampling_method	the sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_args	Sometimes a sampling method can have some extra parameters (eg mcmc sampling)

Note: Maybe the return value would better be a Sample<StateVar> instead of a StateVar

◆ Simulate() [2/2]

MatrixWrapper::ColumnVector Simulate	(	const MatrixWrapper::ColumnVector &	x,
		const SampleMthd	sampling_method = `SampleMthd::DEFAULT`,
		void *	sampling_args = `NULL`
	)

inherited

Simulate the system (no input system)

Parameters

x	current state of the system

Returns: State where we arrive by simulating the measurement model

Note: Maybe the return value would better be a Sample<StateVar> instead of a StateVar

Parameters

sampling_method	the sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_args	Sometimes a sampling method can have some extra parameters (eg mcmc sampling)

Member Data Documentation

◆ _MeasurementPdf

ConditionalPdf<MatrixWrapper::ColumnVector ,MatrixWrapper::ColumnVector >* _MeasurementPdf

protectedinherited

ConditionalPdf representing $P(Z_k | X_{k}, U_{k})$ .

Definition at line 62 of file measurementmodel.h.

◆ _systemWithoutSensorParams

bool _systemWithoutSensorParams

protectedinherited

System with no sensor params??

Definition at line 65 of file measurementmodel.h.

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

linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LinearAnalyticMeasurementModelGaussianUncertainty_Implicit()

Member Function Documentation

◆ CovarianceGet() [1/2]

◆ CovarianceGet() [2/2]

◆ df_dxGet()

◆ df_dzGet()

◆ HSet()

◆ JSet()

◆ MeasurementPdfSet()

◆ PredictionGet()

◆ ProbabilityGet() [1/2]

◆ ProbabilityGet() [2/2]

◆ Simulate() [1/2]

◆ Simulate() [2/2]

Member Data Documentation

◆ _MeasurementPdf

◆ _systemWithoutSensorParams