Development of pseudo-celestial navigation and tracking system for planar air-bearing satellite simulators
Bibliographic record
Abstract
This paper presents the development of a planar air-bearing microgravity testbed integrated with a pseudo-celestial navigation and tracking system onboard the satellite simulators. The developed navigation system utilizes a low-cost onboard camera as star tracker and a pre-calibrated pseudo-celestial field positioned above the testbed. This approach eliminates the need for expensive, external, and centralized observation and tracking systems. By processing star constellation images onboard with a Kalman filter, each satellite simulator can accurately and stably estimate its pose and velocity in real time. To address challenges associated with discrete gas thrust with on/off binary output and the low update rate of pseudo-celestial navigation, the tracking system integrates an anti-saturation control algorithm with a composite closed-loop control strategy to enhance pose control robustness and improve system responsiveness. The accuracy and stability of the navigation system are validated experimentally with static pose errors of 0.05 mm and 0.005° and dynamic pose errors of 1.2 mm and 0.5°. The efficacy of the control algorithm is also validated by trajectory tracking experiments on the air-bearing table. Furthermore, a cooperative formation flying experiment with two simulators demonstrates that the pseudo-celestial navigation system can be easily scaled for multi-simulator formations. Compared to existing solutions that rely on external and centralized observation systems, this approach proves to be more efficient and effective for synchronized multi-simulator formation flight experiments.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".