Package org.orekit.estimation.sequential

Class KalmanEstimatorUtil

java.lang.Object
org.orekit.estimation.sequential.KalmanEstimatorUtil
public class KalmanEstimatorUtil extends Object
Utility class for Kalman Filter.

This class includes common methods used by the different Kalman models in Orekit (i.e., Extended, Unscented, and Semi-analytical)

Since:
11.3

  • Method Details

    • decorate

      public static MeasurementDecorator decorate(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate)
      Decorate an observed measurement.

      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.

      Parameters:
      observedMeasurement - the measurement
      referenceDate - reference date
      Returns:
      decorated measurement

    • decorateUnscented

      public static MeasurementDecorator decorateUnscented(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate)
      Decorate an observed measurement for an Unscented Kalman Filter.

      This method uses directly the measurement's covariance matrix, without any normalization.

      Parameters:
      observedMeasurement - the measurement
      referenceDate - reference date
      Returns:
      decorated measurement
      Since:
      11.3.2

    • checkDimension

      public static void checkDimension(int dimension, ParameterDriversList orbitalParameters, ParameterDriversList propagationParameters, ParameterDriversList measurementParameters)
      Check dimension.
      Parameters:
      dimension - dimension to check
      orbitalParameters - orbital parameters
      propagationParameters - propagation parameters
      measurementParameters - measurements parameters

    • filterRelevant

      public static SpacecraftState[] filterRelevant(ObservedMeasurement<?> observedMeasurement, SpacecraftState[] allStates)
      Filter relevant states for a measurement.
      Parameters:
      observedMeasurement - measurement to consider
      allStates - all states
      Returns:
      array containing only the states relevant to the measurement

    • applyDynamicOutlierFilter

      public static <T extends ObservedMeasurement<T>> void applyDynamicOutlierFilter(EstimatedMeasurement<T> measurement, RealMatrix innovationCovarianceMatrix)
      Set and apply a dynamic outlier filter on a measurement.

      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.

      Type Parameters:
      T - the type of measurement
      Parameters:
      measurement - measurement to filter
      innovationCovarianceMatrix - 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

    • computeInnovationVector

      public static RealVector computeInnovationVector(EstimatedMeasurement<?> predicted)
      Compute the unnormalized innovation vector from the given predicted measurement.
      Parameters:
      predicted - predicted measurement
      Returns:
      the innovation vector

    • computeInnovationVector

      public static RealVector computeInnovationVector(EstimatedMeasurement<?> predicted, double[] sigma)
      Compute the normalized innovation vector from the given predicted measurement.
      Parameters:
      predicted - predicted measurement
      sigma - measurement standard deviation
      Returns:
      the innovation vector

    • normalizeCovarianceMatrix

      public static RealMatrix normalizeCovarianceMatrix(RealMatrix physicalP, double[] parameterScales)
      Normalize a covariance matrix.
      Parameters:
      physicalP - "physical" covariance matrix in input
      parameterScales - scale factor of estimated parameters
      Returns:
      the normalized covariance matrix

    • unnormalizeCovarianceMatrix

      public static RealMatrix unnormalizeCovarianceMatrix(RealMatrix normalizedP, double[] parameterScales)
      Un-nomalized the covariance matrix.
      Parameters:
      normalizedP - normalized covariance matrix
      parameterScales - scale factor of estimated parameters
      Returns:
      the un-normalized covariance matrix

    • unnormalizeStateTransitionMatrix

      public static RealMatrix unnormalizeStateTransitionMatrix(RealMatrix normalizedSTM, double[] parameterScales)
      Un-nomalized the state transition matrix.
      Parameters:
      normalizedSTM - normalized state transition matrix
      parameterScales - scale factor of estimated parameters
      Returns:
      the un-normalized state transition matrix

    • unnormalizeMeasurementJacobian

      public static RealMatrix unnormalizeMeasurementJacobian(RealMatrix normalizedH, double[] parameterScales, double[] sigmas)
      Un-normalize the measurement matrix.
      Parameters:
      normalizedH - normalized measurement matrix
      parameterScales - scale factor of estimated parameters
      sigmas - measurement theoretical standard deviation
      Returns:
      the un-normalized measurement matrix

    • unnormalizeInnovationCovarianceMatrix

      public static RealMatrix unnormalizeInnovationCovarianceMatrix(RealMatrix normalizedS, double[] sigmas)
      Un-normalize the innovation covariance matrix.
      Parameters:
      normalizedS - normalized innovation covariance matrix
      sigmas - measurement theoretical standard deviation
      Returns:
      the un-normalized innovation covariance matrix

    • unnormalizeKalmanGainMatrix

      public static RealMatrix unnormalizeKalmanGainMatrix(RealMatrix normalizedK, double[] parameterScales, double[] sigmas)
      Un-normalize the Kalman gain matrix.
      Parameters:
      normalizedK - normalized Kalman gain matrix
      parameterScales - scale factor of estimated parameters
      sigmas - measurement theoretical standard deviation
      Returns:
      the un-normalized Kalman gain matrix