Package org.orekit.rugged.adjustment

Class AdjustmentContext

java.lang.Object

org.orekit.rugged.adjustment.AdjustmentContext

public class AdjustmentContext
extends Object

Create adjustment context for viewing model refining.

Since:

2.0

Author:

Lucie LabatAllee, Jonathan Guinet, Luc Maisonobe, Guylaine Prat

Constructor Summary

Constructors

Constructor	Description
AdjustmentContext(Collection<Rugged> viewingModel, Observables measurements)	Build a new instance.

Method Summary

Modifier and Type	Method	Description
LeastSquaresOptimizer.Optimum	estimateFreeParameters(Collection<String> ruggedNameList, int maxEvaluations, double parametersConvergenceThreshold)	Estimate the free parameters in viewing model to match specified sensor to ground mappings.
void	setOptimizer(OptimizerId optimizerId)	Setter for optimizer algorithm.

Methods inherited from class java.lang.Object

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

Constructor Detail

AdjustmentContext

public AdjustmentContext(Collection<Rugged> viewingModel,
                         Observables measurements)

Build a new instance. The default optimizer is Gauss Newton with QR decomposition.

Parameters:

viewingModel - viewing model

measurements - control and tie points

Method Detail

setOptimizer

public void setOptimizer(OptimizerId optimizerId)

Setter for optimizer algorithm.

Parameters:

optimizerId - the chosen algorithm

estimateFreeParameters

public LeastSquaresOptimizer.Optimum estimateFreeParameters(Collection<String> ruggedNameList,
                                                            int maxEvaluations,
                                                            double parametersConvergenceThreshold)

Estimate the free parameters in viewing model to match specified sensor to ground mappings.

This method is typically used for calibration of on-board sensor parameters, like rotation angles polynomial coefficients.

Before using this method, the viewing model parameters retrieved by calling the getParametersDrivers() method on the desired sensors must be configured. The parameters that should be estimated must have their selection status set to true whereas the parameters that should retain their current value must have their selection status set to false. If needed, the value of the estimated/selected parameters can also be changed before calling the method, as this value will serve as the initial value in the estimation process.

The method solves a least-squares problem to minimize the residuals between test locations and the reference mappings by adjusting the selected viewing models parameters.

The estimated parameters can be retrieved after the method completes by calling again the getParametersDrivers() method on the desired sensors and checking the updated values of the parameters. In fact, as the values of the parameters are already updated by this method, if users want to use the updated values immediately to perform new direct/inverse locations, they can do so without looking at the parameters: the viewing models are already aware of the updated parameters.

Parameters:

ruggedNameList - list of rugged to refine

maxEvaluations - maximum number of evaluations

parametersConvergenceThreshold - convergence threshold on normalized parameters (dimensionless, related to parameters scales)

Returns:

optimum of the least squares problem