High Performance Fiber Optic Gyroscope-based Attitude Determination and Control System for Autonomous Terrestrial Target Tracking using Small Satellites
Bibliographic record
Abstract
This thesis provides an engineering framework for designing an advanced Attitude Determination and Control Subsystem (ADCS) for small satellite-enabled autonomous target tracking applications from low-Earth orbit requiring a pointing accuracy of ±0.1° (2-σ) and stability of ±0.05 °/s or better. Current Earth-observation satellites designed and built at the University of Toronto Space Flight Laboratory (SFL) are capable of constraining the satellite's fine-pointing accuracy to ±0.3° (2-σ) during terrestrial-target tracking maneuvers. Leveraging high-grade miniaturized and commercially-accessible fiber optic gyroscopes (FOGs), an advanced ADCS compatible with SFL's Next-generation Earth Monitoring and Observation (NEMO) bus and similar micro-satellite platforms, is being developed at the Space Flight Laboratory. High-frequency gyro measurements are used to augment lower-cadence star tracker solutions in an extended Kalman filter, providing superior attitude and rate estimates. To assess the absolute pointing performance of this FOG-based control system, high-fidelity ground-based target tracking simulations were conducted for fixed and moving targets.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".