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

#include <linearanalyticmeasurementmodel_gaussianuncertainty.h>

Inheritance diagram for LinearAnalyticMeasurementModelGaussianUncertainty:

Public Member Functions
	LinearAnalyticMeasurementModelGaussianUncertainty (LinearAnalyticConditionalGaussian *pdf=NULL)
	Constructor.

virtual MatrixWrapper::Matrix	df_dxGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
	Returns H-matrix.

virtual MatrixWrapper::ColumnVector	PredictionGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
	Returns estimation of measurement.

virtual MatrixWrapper::SymmetricMatrix	CovarianceGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
	Returns covariance on the measurement.

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 measurementmodels of the form

$z_k = H \times x_k + J \times s_{k} + N(\mu,\Sigma)$

Definition at line 32 of file linearanalyticmeasurementmodel_gaussianuncertainty.h.

Constructor & Destructor Documentation

◆ LinearAnalyticMeasurementModelGaussianUncertainty()

LinearAnalyticMeasurementModelGaussianUncertainty ( LinearAnalyticConditionalGaussian * pdf = NULL )

Constructor.

Parameters

pdf	Conditional pdf, with Gaussian uncertainty

Member Function Documentation

◆ CovarianceGet()

virtual MatrixWrapper::SymmetricMatrix CovarianceGet	(	const MatrixWrapper::ColumnVector &	u,
		const MatrixWrapper::ColumnVector &	x
	)

virtual

Returns covariance on the measurement.

Reimplemented from AnalyticMeasurementModelGaussianUncertainty.

Reimplemented in LinearAnalyticMeasurementModelGaussianUncertainty_Implicit.

◆ df_dxGet()

virtual MatrixWrapper::Matrix df_dxGet	(	const MatrixWrapper::ColumnVector &	u,
		const MatrixWrapper::ColumnVector &	x
	)

virtual

Returns H-matrix.

$H = \frac{df}{dx} \mid_{u,x}$

used by extended kalman filter

Parameters

u	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 AnalyticMeasurementModelGaussianUncertainty.

Reimplemented in LinearAnalyticMeasurementModelGaussianUncertainty_Implicit.

◆ HSet()

void HSet ( const MatrixWrapper::Matrix & h )

Set Matrix H.

This can be particularly useful for time-varying systems

Parameters

h Matrix H

◆ JSet()

void JSet ( const MatrixWrapper::Matrix & j )

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 &	u,
		const MatrixWrapper::ColumnVector &	x
	)

virtual

Returns estimation of measurement.

Reimplemented from AnalyticMeasurementModelGaussianUncertainty.

Reimplemented in LinearAnalyticMeasurementModelGaussianUncertainty_Implicit.

◆ 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.h

Public Member Functions

Protected Attributes

Detailed Description

Constructor & Destructor Documentation

◆ LinearAnalyticMeasurementModelGaussianUncertainty()

Member Function Documentation

◆ CovarianceGet()

◆ df_dxGet()

◆ HSet()

◆ JSet()

◆ MeasurementPdfSet()

◆ PredictionGet()

◆ ProbabilityGet() [1/2]

◆ ProbabilityGet() [2/2]

◆ Simulate() [1/2]

◆ Simulate() [2/2]

Member Data Documentation

◆ _MeasurementPdf

◆ _systemWithoutSensorParams