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 Summary

Modifier and Type	Method and Description
static <T extends ObservedMeasurement<T>> void	applyDynamicOutlierFilter(EstimatedMeasurement<T> measurement, org.hipparchus.linear.RealMatrix innovationCovarianceMatrix) Set and apply a dynamic outlier filter on a measurement.
static void	checkDimension(int dimension, ParameterDriversList orbitalParameters, ParameterDriversList propagationParameters, ParameterDriversList measurementParameters) Check dimension.
static org.hipparchus.linear.RealVector	computeInnovationVector(EstimatedMeasurement<?> predicted) Compute the unnormalized innovation vector from the given predicted measurement.
static org.hipparchus.linear.RealVector	computeInnovationVector(EstimatedMeasurement<?> predicted, double[] sigma) Compute the normalized innovation vector from the given predicted measurement.
static MeasurementDecorator	decorate(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate) Decorate an observed measurement.
static MeasurementDecorator	decorateUnscented(ObservedMeasurement<?> observedMeasurement, AbsoluteDate referenceDate) Decorate an observed measurement for an Unscented Kalman Filter.
static SpacecraftState[]	filterRelevant(ObservedMeasurement<?> observedMeasurement, SpacecraftState[] allStates) Filter relevant states for a measurement.
static org.hipparchus.linear.RealMatrix	normalizeCovarianceMatrix(org.hipparchus.linear.RealMatrix physicalP, double[] parameterScales) Normalize a covariance matrix.
static org.hipparchus.linear.RealMatrix	unnormalizeCovarianceMatrix(org.hipparchus.linear.RealMatrix normalizedP, double[] parameterScales) Un-nomalized the covariance matrix.
static org.hipparchus.linear.RealMatrix	unnormalizeInnovationCovarianceMatrix(org.hipparchus.linear.RealMatrix normalizedS, double[] sigmas) Un-normalize the innovation covariance matrix.
static org.hipparchus.linear.RealMatrix	unnormalizeKalmanGainMatrix(org.hipparchus.linear.RealMatrix normalizedK, double[] parameterScales, double[] sigmas) Un-normalize the Kalman gain matrix.
static org.hipparchus.linear.RealMatrix	unnormalizeMeasurementJacobian(org.hipparchus.linear.RealMatrix normalizedH, double[] parameterScales, double[] sigmas) Un-normalize the measurement matrix.
static org.hipparchus.linear.RealMatrix	unnormalizeStateTransitionMatrix(org.hipparchus.linear.RealMatrix normalizedSTM, double[] parameterScales) Un-nomalized the state transition matrix.

Methods inherited from class java.lang.Object

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

Method Detail

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,
                                                                                org.hipparchus.linear.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 org.hipparchus.linear.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 org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix normalizeCovarianceMatrix(org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix unnormalizeCovarianceMatrix(org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix unnormalizeStateTransitionMatrix(org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix unnormalizeMeasurementJacobian(org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix unnormalizeInnovationCovarianceMatrix(org.hipparchus.linear.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 org.hipparchus.linear.RealMatrix unnormalizeKalmanGainMatrix(org.hipparchus.linear.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