public class KalmanEstimatorUtil extends Object
This class includes common methods used by the different Kalman models in Orekit (i.e., Extended, Unscented, and Semi-analytical)
Modifier and Type | Method and Description |
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static <T extends ObservedMeasurement<T>> |
applyDynamicOutlierFilter(EstimatedMeasurement<T> measurement,
RealMatrix innovationCovarianceMatrix)
Set and apply a dynamic outlier filter on a measurement.
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static void |
checkDimension(int dimension,
ParameterDriversList orbitalParameters,
ParameterDriversList propagationParameters,
ParameterDriversList measurementParameters)
Check dimension.
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static RealVector |
computeInnovationVector(EstimatedMeasurement<?> predicted)
Compute the unnormalized innovation vector from the given predicted measurement.
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static RealVector |
computeInnovationVector(EstimatedMeasurement<?> predicted,
double[] sigma)
Compute the normalized innovation vector from the given predicted measurement.
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static MeasurementDecorator |
decorate(ObservedMeasurement<?> observedMeasurement,
AbsoluteDate referenceDate)
Decorate an observed measurement.
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static SpacecraftState[] |
filterRelevant(ObservedMeasurement<?> observedMeasurement,
SpacecraftState[] allStates)
Filter relevant states for a measurement.
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public static MeasurementDecorator decorate(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate)
The "physical" measurement noise matrix is the covariance matrix of the measurement. Normalizing it consists in applying the following equation: Rn[i,j] = R[i,j]/σ[i]/σ[j] Thus the normalized measurement noise matrix is the matrix of the correlation coefficients between the different components of the measurement.
observedMeasurement
- the measurementreferenceDate
- reference datepublic static void checkDimension(int dimension, ParameterDriversList orbitalParameters, ParameterDriversList propagationParameters, ParameterDriversList measurementParameters)
dimension
- dimension to checkorbitalParameters
- orbital parameterspropagationParameters
- propagation parametersmeasurementParameters
- measurements parameterspublic static SpacecraftState[] filterRelevant(ObservedMeasurement<?> observedMeasurement, SpacecraftState[] allStates)
observedMeasurement
- measurement to considerallStates
- all statespublic static <T extends ObservedMeasurement<T>> void applyDynamicOutlierFilter(EstimatedMeasurement<T> measurement, RealMatrix innovationCovarianceMatrix)
Loop on the modifiers to see if a dynamic outlier filter needs to be applied.
Compute the sigma array using the matrix in input and set the filter.
Apply the filter by calling the modify method on the estimated measurement.
Reset the filter.
T
- the type of measurementmeasurement
- measurement to filterinnovationCovarianceMatrix
- So called innovation covariance matrix S, with:S = H.Ppred.Ht + R
Where:
- H is the normalized measurement matrix (Ht its transpose)
- Ppred is the normalized predicted covariance matrix
- R is the normalized measurement noise matrix
public static RealVector computeInnovationVector(EstimatedMeasurement<?> predicted)
predicted
- predicted measurementpublic static RealVector computeInnovationVector(EstimatedMeasurement<?> predicted, double[] sigma)
predicted
- predicted measurementsigma
- measurement standard deviationCopyright © 2002-2022 CS GROUP. All rights reserved.