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LinearAnalyticMeasurementModelGaussianUncertainty Class Reference

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

#include <linearanalyticmeasurementmodel_gaussianuncertainty.h>

Inheritance diagram for LinearAnalyticMeasurementModelGaussianUncertainty:
AnalyticMeasurementModelGaussianUncertainty MeasurementModel< MatrixWrapper::ColumnVector, MatrixWrapper::ColumnVector > LinearAnalyticMeasurementModelGaussianUncertainty_Implicit

Public Member Functions

 LinearAnalyticMeasurementModelGaussianUncertainty (LinearAnalyticConditionalGaussian *pdf=NULL)
 Constructor. More...
 
virtual MatrixWrapper::Matrix df_dxGet (const MatrixWrapper::ColumnVector &u, const MatrixWrapper::ColumnVector &x)
 Returns H-matrix. More...
 
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. More...
 
void JSet (const MatrixWrapper::Matrix &j)
 Set Matrix J. More...
 
const MatrixWrapper::MatrixHGet () const
 Get Matrix H.
 
const MatrixWrapper::MatrixJGet () 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. More...
 
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. More...
 
MatrixWrapper::ColumnVector Simulate (const MatrixWrapper::ColumnVector &x, int sampling_method=DEFAULT, void *sampling_args=NULL)
 Simulate the system (no input system) More...
 
Probability ProbabilityGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x, const MatrixWrapper::ColumnVector &s)
 Get the probability of a certain measurement. More...
 
Probability ProbabilityGet (const MatrixWrapper::ColumnVector &z, const MatrixWrapper::ColumnVector &x)
 Get the probability of a certain measurement. More...
 

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

Constructor.

Parameters
pdfConditional pdf, with Gaussian uncertainty

Member Function Documentation

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
uThe value of the input in which the derivate is evaluated
xThe 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
hMatrix H
void JSet ( const MatrixWrapper::Matrix j)

Set Matrix J.

This can be particularly useful for time-varying systems

Parameters
jMatrix J
void MeasurementPdfSet ( ConditionalPdf< MatrixWrapper::ColumnVector , MatrixWrapper::ColumnVector > *  pdf)
inherited

Set the MeasurementPDF.

Parameters
pdfa 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
zthe measurement value
xcurrent state of the system
sthe 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
zthe measurement value
xx 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
xcurrent state of the system
ssensor parameter
Returns
Measurement generated by simulating the measurement model
Parameters
sampling_methodthe sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_argsSometimes 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
xcurrent 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_methodthe sampling method to be used while sampling from the Conditional Pdf describing the system (if not specified = DEFAULT)
sampling_argsSometimes a sampling method can have some extra parameters (eg mcmc sampling)

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