Python Simple Covariance propagation

Hi all,
I am implementing the numerical propagation following this official tutorial:
examples/Example_numerical_prop.ipynb · master · Orekit Labs / Orekit Python Wrapper · GitLab

Having the matrix process noise Q , I would like to know if in Orekit is possible to implement directly the predicted covariance propagation.
I mean a sort of prediction step of the kalman filter, or simular.

Many thanks for your time and help
Best Regards

Hi @AlessandroV,

There is a CovariancePropagation tutorial. It uses the StateCovarianceMatrixProvider to perform a linear propagation of the covariance.

With two limitations with respect to what is available in the Kalman filters:

  • Only the orbital covariance is propagated (i.e. the propagation and measurements parameters aren’t propagated)
  • You will have to add the process noise matrix Q by yourself after propagation


thank you very much