Advanced Sun-Sensor Processing and Design for Super-Resolution Performance
Bibliographic record
Abstract
We analyze the performance of conventional and parametric super-resolution algorithms for estimating sun position in a spacecraft sun-sensor. Widely employed in other applications, we examine whether parametric algorithms can increase sensor performance without affecting the cost of the sensor system. Using a simplified model of detector illumination our simulations provide quantitative comparisons of algorithm performance and assess how simple sensor redesigns will further improve the net system performance. The first set of tests evaluates the effect of increased noise on the performance of each algorithm for both narrow-or wide-pattern, and one- or two-slit detector illumination patterns. Our findings show that parametric algorithms display very good performance throughout the test regime, particularly when using wide-pattern illumination. Better than two-fold resolution improvements over high-accuracy traditional algorithms are observed in the presence of realistic system noise. Further tests establish that multiple-peak illumination patterns enhance resolution, while wide peaks generally are impairment. These mask-dependent improvements are observed in both of the parametric algorithms and one of the traditional algorithms
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".