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

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::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.
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 $ 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:
pdf Conditional 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:
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

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)

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


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

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