Add Process Noise to Generic Covariance Propagation

Hey all. I am currently dealing with a problem where I have to regularly add a certain degree of process noise to the propagation of a covariance matrix over time. W.R.T. a Kalman Filter obviously Orekit already accounts for this, but I am simply propagating an initial state into the future with no measurements to feed into a Kalman filter for comparison. I know how to implement this manually mathematically, but I was thinking it might be a nice feature to be able to implement it automatically in future versions of Orekit.

I did think about running the Kalman filter with some sort of dummy measurement and adding process noise to the covariance that way, but I couldn’t think of any dummy measurement that wouldn’t affect the under-the-hood propagated covariance, and I was afraid that if I didn’t affect the numbers being propagated by Orekit directly I’d get increasingly wrong results as the propagator got farther from origin.

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This seems interesting, contribution welcome!