LinearAnalyticMeasurementModelGaussianUncertainty Class Reference

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

#include <linearanalyticmeasurementmodel_gaussianuncertainty.h>

Inheritance diagram for LinearAnalyticMeasurementModelGaussianUncertainty:

List of all members.

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, int sampling_method=DEFAULT, void *sampling_args=NULL)
	Simulate the Measurement, given a certain state, and an input.
MatrixWrapper::ColumnVector	Simulate (const MatrixWrapper::ColumnVector &x, int sampling_method=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 .
bool	_systemWithoutSensorParams
	System with no sensor params??

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Detailed Description

Class for linear analytic measurementmodels with additive gaussian noise.

This class represents all measurementmodels of the form

Definition at line 32 of file linearanalyticmeasurementmodel_gaussianuncertainty.h.

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Constructor & Destructor Documentation

LinearAnalyticMeasurementModelGaussianUncertainty

(

LinearAnalyticConditionalGaussian *

pdf = NULL

)

Constructor.

Parameters:

pdf	Conditional pdf, with Gaussian uncertainty

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Member Function Documentation

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

Returns H-matrix.

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.

void HSet

(

const MatrixWrapper::Matrix &

h

)

Set Matrix H.

This can be particularly useful for time-varying systems

Parameters:

h

Matrix H

void JSet

(

const MatrixWrapper::Matrix &

j

)

Set Matrix J.

This can be particularly useful for time-varying systems

Parameters:

j

Matrix J

void MeasurementPdfSet

(

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

pdf

)

[inherited]

Set the MeasurementPDF.

Parameters:

pdf	a pointer to the measurement pdf

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

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

MatrixWrapper::ColumnVector Simulate	(	const MatrixWrapper::ColumnVector &	x,
		const MatrixWrapper::ColumnVector &	s,
		int	sampling_method = 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

MatrixWrapper::ColumnVector Simulate	(	const MatrixWrapper::ColumnVector &	x,
		int	sampling_method = 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)

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

• linearanalyticmeasurementmodel_gaussianuncertainty.h