Improving calibration and alignment observability for star trackers
Bibliographic record
Abstract
Linear dependencies between the parameters that characterize instrument behaviour create difficulty when calibrating sensors such as star trackers and sun sensors. Poorly observable model formulations can lead to poor convergence and repeatability when trying to solve for an optimal set of parameter values. Even when parameter optimizations converge, the physical interpretation of the optimal parameter values is often dubious. Although the difficulties arising from linearly dependent optical models have been acknowledged for some time, calibration models featuring poor parameter observability frequently appear in both the space engineering and machine vision literature. In this study we present a general framework for recognizing, assessing, and mitigating the effects of parameter interdependencies. We draw on popular optical instrument models from literature and examine them against several star tracker calibration datasets obtained from laboratory testing. The calibrations datasets include duplicate calibrations of the same instrument as well as calibrations over a range of different operating temperatures. We assess the performance of the extant models and explore the tradeoff between minimizing residual errors and improving numerical conditioning.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".