Package org.orekit.rugged.adjustment

Class InterSensorsOptimizationProblemBuilder

java.lang.Object

org.orekit.rugged.adjustment.InterSensorsOptimizationProblemBuilder

public class InterSensorsOptimizationProblemBuilder
extends Object

Constructs the optimization problem for a list of tie points.

Since:

2.0

Author:

Guylaine Prat, Lucie Labat Allee, Jonathan Guinet, Luc Maisonobe

Field Summary

Fields

Modifier and Type	Field	Description
protected static int	ESTIMATION_LINE_RANGE_MARGIN	Margin used in parameters estimation for the inverse location lines range.

Constructor Summary

Constructors

Constructor	Description
InterSensorsOptimizationProblemBuilder(List<LineSensor> sensors, Observables measurements, Collection<Rugged> ruggedList)	Constructor.

Method Summary

Modifier and Type	Method	Description
org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem	build(int maxEvaluations, double convergenceThreshold)	Least square problem builder.
protected org.hipparchus.optim.nonlinear.vector.leastsquares.MultivariateJacobianFunction	createFunction()	Create the model function value and its Jacobian.
protected void	createTargetAndWeight()	Create targets and weights of optimization problem.
protected List<org.orekit.utils.ParameterDriver>	getDrivers()	Get the parameters drivers list.
protected DSGenerator	getGenerator()	Get the derivative structure generator.
protected Observables	getMeasurements()	Get the measurements.
protected int	getNbParams()	Get the number of parameters to refine.
protected List<LineSensor>	getSensors()	Get the sensors list.
protected void	initMapping()	Parse the observables to select mapping .

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

ESTIMATION_LINE_RANGE_MARGIN

protected static final int ESTIMATION_LINE_RANGE_MARGIN

Margin used in parameters estimation for the inverse location lines range.

See Also:

Constant Field Values

Constructor Detail

InterSensorsOptimizationProblemBuilder

public InterSensorsOptimizationProblemBuilder(List<LineSensor> sensors,
                                              Observables measurements,
                                              Collection<Rugged> ruggedList)

Constructor.

Parameters:

sensors - list of sensors to refine

measurements - set of observables

ruggedList - names of rugged to refine

Method Detail

initMapping

protected void initMapping()

Parse the observables to select mapping .

createTargetAndWeight

protected void createTargetAndWeight()

Create targets and weights of optimization problem.

createFunction

protected org.hipparchus.optim.nonlinear.vector.leastsquares.MultivariateJacobianFunction createFunction()

Create the model function value and its Jacobian.

Returns:

the model function value and its Jacobian

build

public final org.hipparchus.optim.nonlinear.vector.leastsquares.LeastSquaresProblem build(int maxEvaluations,
                                                                                          double convergenceThreshold)

Least square problem builder.

Parameters:

maxEvaluations - maxIterations and evaluations

convergenceThreshold - parameter convergence threshold

Returns:

the least square problem

getSensors

protected List<LineSensor> getSensors()

Get the sensors list.

Returns:

the sensors list

getNbParams

protected final int getNbParams()

Get the number of parameters to refine.

Returns:

the number of parameters to refine

getDrivers

protected final List<org.orekit.utils.ParameterDriver> getDrivers()

Get the parameters drivers list.

Returns:

the selected list of parameters driver

getGenerator

protected final DSGenerator getGenerator()

Get the derivative structure generator.

Returns:

the derivative structure generator.

getMeasurements

protected Observables getMeasurements()

Get the measurements.

Returns:

the measurements