Visual Attitude Determination Strategy for a Nanosatellite Attitude Control Simulator
Bibliographic record
Abstract
Attitude determination is critical in maintaining optimal functionality of a CubeSat and ensuring that the mission objectives are achieved. Thus, attitude determination and control (ADC) algorithms must be developed and rigorously tested prior to implementation. A lab-based Nanosatellite Attitude Control Simulator (NACS) is an effective platform for testing mock attitude control algorithms. The onboard inertial measurement unit (IMU) is a key component in providing attitude estimates, implemented for the measurement of body angular rates and orientation. However, readings are susceptible to drift and accuracy degradation, therefore justifying the development of robust measurement and estimation strategies. While using an off-board sensor is a viable option, it is important to ensure that the readings of such a sensor are accurate to a degree that is sustainably comparable to the initial readings of the IMU. This research discusses the development of an optical attitude estimation setup. Deployment of optical attitude estimation through computer vision eliminates drift and provides consistent estimates for the state vector. Attitude estimates from the optical sensor are subsequently used to compare and demarcate the measurements from the IMU. Additionally, this technique can be adapted for other applications requiring similar three-degree-of-freedom tracking. Further areas of work include the implementation of this sensor as part of a sensor fusion framework.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".