## Add a provider generating process noise increasing in time, for better Kalman filtering

The Kalman filter implementation in Orekit let the user provide the
process noise

matrix at each prediction step. As of 2018-03-22, the only
implementation of

the ProcessNoiseMatricProvider is ConstantProcessNoise. Users could
implement their

own process noise provider, as complex and as realistic as they want.

It would be interesting to have another implementation in Orekit that is
more realistic

than a constant matrix, and that simulates at least the expansion of the
uncertainty

ellipsoid along track as time without measurements increase.

A first idea would be to have user provide six polynomials representing
the diagonal

elements of the covariance in some Local Orbital Frame (say LVLH for
example). The

polynomial for the along velocity covariance would increase faster than
the polynomial

for the across directions. Then, by pre and post-multiplying by the
rotation matrix

we get from LOFType.rotationFromInertial, we get the dense matri in
inertial frame

we need to return.

This implementation is simple but much more realistic than a constant
matrix, and it

should properly take into account the fact the uncertainty from the
prediction step

is larger when the time between measurements is long than when
measurements occur

frequently.

Finding the polynomials would remain here the responsibility of the
caller. Later on,

we can think about using FieldPropagator on a reference orbit to
estimate these

polynomials during the mission analysis phase prior to operations.

*(from redmine: issue id 403, created on 2018-03-22)*