Measurement weighting strategies for satellite attitude estimation
Bibliographic record
Abstract
Most attitude estimation algorithms (e.g, q-Method, QUEST, FOAM, etc.) permit vector observations to be weighted using scalar weights. From a theoretical standpoint, the best choice of weights is clear: when weights are proportional to inverse variance, the Wahba problem solutions are equivalent to the maximum likelihood solution. In practice, the true covariance may be difficult to determine online, and engineers may have to rely on heuristic estimates of the `goodness' of any particular measurement. In this paper, we examine several strategies for determining effective weighting for vector observations and discuss the effects of weighting schemes on system performance. Noise equivalent angle estimates provide the most direct approximations of the measurement covariances needed for optimal weighting. We demonstrate how simple lab measurements can be used to evaluate the variation of centroid noise with star brightness and position in the field of view. We evaluate appropriate fitting functions for the noise estimates and compare the relative merits of scalar and vector measurement weighting. Comparing the noise calibration results from multiple instruments provides insight into the expected performance loss that may be experienced if a per-unit calibration is replaced by simpler relations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".