Multiple Star Trackers: Processing, Pitfalls, and Performance
Bibliographic record
Abstract
Equipping spacecraft with multiple star trackers offers significant benefits for the attitude control engineer. Multiple aperture systems reduce the performance impact of roll-axis imprecision and reduce the likelihood of sun-inclusions causing loss of attitude lock. These benefits have been understood for some time. The wider availability of compact, low power star trackers makes multiple sensor systems increasingly attractive. That said, deriving the full benefit from a multiple star tracker system requires considerable attention to detail.This study examines the practical challenges of multiple star tracker systems. We present a comparison of different architectures for fusing measurements from multiple instruments and outline the key tradeoffs encountered during design. Additionally, using orbital data obtained from an Earth-Observing spacecraft, we examine and show how overlooking even minor factors can lead to significant performance degradation. Diagnosing the sources of increased error can be a challenge because different phenomena can introduce qualitatively similar errors. We highlight the outcome of our orbital study and present a series of insights that will allow designers to make the most effective use of their available sensors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".