Mitigation Strategies for Pattern Mismatching in Star Trackers

Star tracker mismatching, although infrequent, can be detrimental to satellite operation and becomes more likely with worsened conditions including high slew rates, radiation, and stray light. Rocket Lab’s ST-16 Star Trackers use several methods to test for match validity; however, in general, there are trade-offs between matching robustness and hardware performance, and it is impractical to entirely remove the possibility of false positive returns. To understand the types of scenarios where false positives occur, a review of existing datasets from the ST-16 was completed, and false positives were identified. The likelihood of false positive matching grows exceedingly rare as the number of matched stars increases, and the existing onboard matching algorithm accurately rejects marginal three-star matches. Therefore, most observed false returns were comprised of four-star matches. To study the occurrences of different failure methods, a test dataset with a significant number of false-positive returns was generated on-ground using a hardware-in-the-loop simulator. Some of the observed failure methods included matching non-cataloged objects, mismatching similar star patterns, and flat polygons—those with points nearly in line. With this understanding, several mitigation techniques are proposed, including catalog changes that target patterns prone to mismatching, additional filtering, and methods combining both. One such catalog change implements a method using hash tables to evaluate and remove similarities within a star catalog. After implementing these mitigation techniques, the false positive rate was decreased more than 4 times from the control dataset with an increase in true positive availability. These methods were implemented without adding significant processing complexity or increased hardware requirements, enabling future integration with current star trackers in orbit.