Oscar Rodriguez Fernandez, OKAPI:Orbits GmbH; Adrian Diez Martin, OKAPI:Orbits GmbH; Njord Eggen, OKAPI:Orbits GmbH; Christopher Kebschull, OKAPI:Orbits GmbH; Alex Bush, OKAPI:Orbits GmbH
Keywords: SSA, SST, KF, OD, Orbit Determination, Kalman Filter, Covariance realism,
Abstract:
In this work, we present a novel implementation of an Unscented Smith-Kalman Filter (USKF) designed for robust Orbit Determination (OD that includes dynamic consider parameter uncertainty estimation. The algorithm has been validated by processing data from Global Navigation Satellite System (GNSS) and Space Surveillance and Tracking (SST) sensors for several LEO satellites. The entire algorithm is implemented in FORTRAN for fast performance and employs the open sourced NPI Ephemeris Propagation Tool with Uncertainty Extrapolation (NEPTUNE) for high fidelity orbit propagation.
The USKF offers a more precise approach to modeling the process noise of the system as it is linked to the source of the uncertainty, for example, errors in drag acceleration modeling. Our methodology further extends this concept by incorporating dynamic consider-parameter modeling that adapts to time-varying uncertainties and can better deal with anomalous situations, like those triggered by solar storms. These are not captured by conventional methods, like Kalman Filters (KF) and other typical OD approaches, which can lead to filter divergence and/or underestimated covariances.
Date of Conference: September 16-19, 2025
Track: Astrodynamics