Package org.orekit.estimation.sequential

Class AbstractCovarianceMatrixProvider

java.lang.Object

org.orekit.estimation.sequential.AbstractCovarianceMatrixProvider

All Implemented Interfaces:

CovarianceMatrixProvider

Direct Known Subclasses:

ConstantProcessNoise, UnivariateProcessNoise

public abstract class AbstractCovarianceMatrixProvider
extends Object
implements CovarianceMatrixProvider

Abstract provider handling a predefined initial covariance matrix.

This class always provides a predefined initial noise matrix.

Since:

9.2

Author:

Luc Maisonobe

Constructor Summary

Constructors

Modifier	Constructor	Description
protected	AbstractCovarianceMatrixProvider(org.hipparchus.linear.RealMatrix initialNoiseMatrix)	Simple constructor.

Method Summary

Modifier and Type	Method	Description
org.hipparchus.linear.RealMatrix	getInitialCovarianceMatrix(SpacecraftState initial)	Get the initial covariance matrix.

Methods inherited from class java.lang.Object

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

Methods inherited from interface org.orekit.estimation.sequential.CovarianceMatrixProvider

getProcessNoiseMatrix

Constructor Detail

AbstractCovarianceMatrixProvider

protected AbstractCovarianceMatrixProvider(org.hipparchus.linear.RealMatrix initialNoiseMatrix)

Simple constructor.

Parameters:

initialNoiseMatrix - initial process noise

Method Detail

getInitialCovarianceMatrix

public org.hipparchus.linear.RealMatrix getInitialCovarianceMatrix(SpacecraftState initial)

Get the initial covariance matrix.

The initial covariance matrix is a covariance matrix corresponding to the parameters managed by the Kalman estimator. The number of rows/columns and their order are as follows:

• The first 6 components correspond to the 6 orbital parameters of the associated propagator. All 6 parameters must always be present, regardless of the fact they are estimated or not.
• The following components correspond to the subset of propagation parameters of the associated propagator that are estimated.
• The remaining components correspond to the subset of measurements parameters that are estimated, considering all measurements, even the ones that correspond to spacecrafts not related to the associated propagator

In most cases, the initial covariance matrix will be the output matrix of a previous run of the Kalman filter.

Specified by:

getInitialCovarianceMatrix in interface CovarianceMatrixProvider

Parameters:

initial - initial state state

Returns:

physical (i.e. non normalized) initial covariance matrix

See Also:

PropagatorBuilder.getOrbitalParametersDrivers(), PropagatorBuilder.getPropagationParametersDrivers()