Bayesian Filtering Library Generated from SVN r
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Class for linear analytic measurementmodels with additive gaussian noise. More...
#include <linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h>
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 ![]() | |
bool | _systemWithoutSensorParams |
System with no sensor params?? | |
Class for linear analytic measurementmodels with additive gaussian noise.
This class represents all measurement models of the form
as a linear measurement model with virtual measurement z_k^{virtual}
Definition at line 37 of file linearanalyticmeasurementmodel_gaussianuncertainty_implicit.h.
LinearAnalyticMeasurementModelGaussianUncertainty_Implicit | ( | LinearAnalyticConditionalGaussian * | ) |
Constructor.
Conditional pdf, with Gaussian uncertainty |
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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:
with noise
and covariance
and R the noise on the measurements z .
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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:
with noise
and covariance
and R the noise on the measurements z .
Reimplemented from LinearAnalyticMeasurementModelGaussianUncertainty.
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pure virtual |
Returns H-matrix calculated with measurement z and state x.
used to determine the covariance of noise on the linear measurement equation
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.
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pure virtual |
Returns D-matrix calculated with measurement z and state x.
used to determine the covariance of noise on the linear measurement equation
z | The value of the input in which the derivate is evaluated |
x | The value in the state in which the derivate is evaluated |
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inherited |
Set Matrix H.
This can be particularly useful for time-varying systems
h | Matrix H |
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inherited |
Set Matrix J.
This can be particularly useful for time-varying systems
j | Matrix J |
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inherited |
Set the MeasurementPDF.
a pointer to the measurement pdf |
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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.
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inherited |
Get the probability of a certain measurement.
(measurement independent of input) gived a certain state and input
z | the measurement value |
x | x current state of the system |
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inherited |
Get the probability of a certain measurement.
given a certain state and input
z | the measurement value |
x | current state of the system |
s | the sensor param value |
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inherited |
Simulate the Measurement, given a certain state, and an input.
x | current state of the system |
s | sensor parameter |
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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inherited |
Simulate the system (no input system)
x | current state of the system |
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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protectedinherited |
ConditionalPdf representing
Definition at line 62 of file measurementmodel.h.
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protectedinherited |
System with no sensor params??
Definition at line 65 of file measurementmodel.h.