This paper introduces two new algorithms to accurately estimate Kalman filter process noise 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 discrete time process noise covariance can be estimated through adaptive filtering. However, current adaptive filtering techniques often use ad hoc methods to ensure the estimated process noise covariance is positive semi-definite, and they cannot accurately extrapolate over measurement outages. Furthermore, 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 techniques that can accurately extrapolate the discrete time process noise covariance over gaps in measurements. The benefits of the proposed algorithms are demonstrated through two case studies: an illustrative one-dimensional example and the autonomous navigation of two spacecraft orbiting an asteroid.
- Pub Date:
- September 2019
- Electrical Engineering and Systems Science - Signal Processing;
- Mathematics - Dynamical Systems
- The previous version is to be published in the proceedings of the 2019 AAS/AIAA Astrodynamics Specialist Conference which took place in Portland, ME on August 11-15, 2019. The current version was submitted for publication in the Journal of Guidance, Control, and Dynamics. The journal version includes additional simulations in Case Study II