Uses of Class
org.orekit.estimation.sequential.MeasurementDecorator

Packages that use MeasurementDecorator

Package	Description
org.orekit.estimation.sequential	The sequential package provides an implementation of a Kalman Filter engine to perform an orbit determination.

Uses of MeasurementDecorator in org.orekit.estimation.sequential

Methods in org.orekit.estimation.sequential that return MeasurementDecorator

Modifier and Type	Method	Description
static MeasurementDecorator	KalmanEstimatorUtil.decorate(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate)	Decorate an observed measurement.
static MeasurementDecorator	KalmanEstimatorUtil.decorateUnscented(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate)	Decorate an observed measurement for an Unscented Kalman Filter.

Methods in org.orekit.estimation.sequential with parameters of type MeasurementDecorator

Modifier and Type	Method	Description
NonLinearEvolution	KalmanModel.getEvolution(double previousTime, RealVector previousState, MeasurementDecorator measurement)
NonLinearEvolution	SemiAnalyticalKalmanModel.getEvolution(double previousTime, RealVector previousState, MeasurementDecorator measurement)
UnscentedEvolution	SemiAnalyticalUnscentedKalmanModel.getEvolution(double previousTime, RealVector[] sigmaPoints, MeasurementDecorator measurement)
UnscentedEvolution	UnscentedKalmanModel.getEvolution(double previousTime, RealVector[] sigmaPoints, MeasurementDecorator measurement)
RealVector	KalmanModel.getInnovation(MeasurementDecorator measurement, NonLinearEvolution evolution, RealMatrix innovationCovarianceMatrix)
RealVector	SemiAnalyticalKalmanModel.getInnovation(MeasurementDecorator measurement, NonLinearEvolution evolution, RealMatrix innovationCovarianceMatrix)
RealVector	SemiAnalyticalUnscentedKalmanModel.getInnovation(MeasurementDecorator measurement, RealVector predictedMeas, RealVector predictedState, RealMatrix innovationCovarianceMatrix)
RealVector	UnscentedKalmanModel.getInnovation(MeasurementDecorator measurement, RealVector predictedMeas, RealVector predictedState, RealMatrix innovationCovarianceMatrix)
RealVector[]	SemiAnalyticalUnscentedKalmanModel.getPredictedMeasurements(RealVector[] predictedSigmaPoints, MeasurementDecorator measurement)
RealVector[]	UnscentedKalmanModel.getPredictedMeasurements(RealVector[] predictedSigmaPoints, MeasurementDecorator measurement)

Method parameters in org.orekit.estimation.sequential with type arguments of type MeasurementDecorator

Modifier and Type	Method	Description
DSSTPropagator	SemiAnalyticalKalmanModel.processMeasurements(List<ObservedMeasurement<?>> observedMeasurements, ExtendedKalmanFilter<MeasurementDecorator> filter)	Process a single measurement.
DSSTPropagator	SemiAnalyticalUnscentedKalmanModel.processMeasurements(List<ObservedMeasurement<?>> observedMeasurements, UnscentedKalmanFilter<MeasurementDecorator> filter)	Process measurements.

Constructor parameters in org.orekit.estimation.sequential with type arguments of type MeasurementDecorator

Constructor	Description
SemiAnalyticalMeasurementHandler(SemiAnalyticalProcess model, KalmanFilter<MeasurementDecorator> filter, List<ObservedMeasurement<?>> observedMeasurements, AbsoluteDate referenceDate)	Simple constructor.
SemiAnalyticalMeasurementHandler(SemiAnalyticalProcess model, KalmanFilter<MeasurementDecorator> filter, List<ObservedMeasurement<?>> observedMeasurements, AbsoluteDate referenceDate, boolean isUnscented)	Simple constructor.