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 Details

    • AbstractCovarianceMatrixProvider

      protected AbstractCovarianceMatrixProvider(RealMatrix initialNoiseMatrix)
      Simple constructor.
      Parameters:
      initialNoiseMatrix - initial process noise

  • Method Details

    • getInitialCovarianceMatrix

      public 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: