  This paper introduces two new algorithms to accurately estimate the process
noise covariance of a discrete-time Kalman filter online for robust orbit
determination in the presence of dynamics model uncertainties. Common orbit
determination process noise techniques, such as state noise compensation and
dynamic model compensation, require offline tuning and a priori knowledge of
the dynamical environment. Alternatively, the process noise covariance can be
estimated through adaptive filtering. However, many adaptive filtering
techniques are not applicable to onboard orbit determination due to
computational cost or the assumption of a linear time-invariant system.
Furthermore, existing adaptive filtering techniques do not constrain the
process noise covariance according to the underlying continuous-time dynamical
model, and there has been limited work on adaptive filtering with colored
process noise. To overcome these limitations, a novel approach is developed
which optimally fuses state noise compensation and dynamic model compensation
with covariance matching adaptive filtering. This yields two adaptive and
dynamically constrained process noise covariance estimation techniques. Unlike
many adaptive filtering approaches, the new techniques accurately extrapolate
over measurement outages and do not rely on ad hoc methods to ensure the
process noise covariance is positive semi-definite. The benefits of the
proposed algorithms are demonstrated through two case studies: an illustrative
linear system and the autonomous navigation of two spacecraft orbiting an
asteroid.
