Bibliographic record
Abstract
Recent demand from the small satellite community has led to the development of a new series of star trackers that are specifically designed for small satellites. These units represent substantial improvements in mass, power consumption and cost over traditional star trackers, but suffer slightly in terms of accuracy and availability performance. The primary factors inhibiting their performance are the use of significantly smaller optics, and commercial off the shelf components (COTS). This thesis presents a series of strategies for improving the performance of small satellite star trackers (SSSTs). These goals are realized through the development of offline calibration procedures, flight software, validation tests, and optical trade studies to guide future development. This thesis begins with the development of a target-based focusing procedure that enables precision control over the focus of the sensor optics. This improves the detection performance for dim stars, and ultimately increases the availability of the attitude solution. Flight software is developed to compensate for the effects of electronic rolling shutters, which reside on most COTS image detectors. Combined with a developed camera calibration procedure, these tools reduce the uncertainty with which a star tracker can measure the direction vectors to stars in view, ultimately increasing sensor accuracy. Integrated tests are performed to validate detection performance in dynamic conditions. These tests specifically examine the effect of slew rate on star tracker detection, and availability performance. Lastly, this thesis presents a series of optical trades studies that seek to identify design requirements for high performance SSSTs. The trends in availability and accuracy performance are examined as a function of different lens/detector configurations as well dual/triple-head sensor configurations. Together, these strategies represent tools that aim to improve small satellite star tracker performance and guide future sensor development.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".