Comparison of Relative Mean Orbital Element Estimation Methods for Spacecraft Formation Flying
Bibliographic record
Abstract
Accurate relative state estimation is a necessity for precision spacecraft formation ying. Inaccurate estimation can lead to excessive fuel use and possible spacecraft collision. Control of mean di erential elements has been identi ed as a promising formation-keeping strategy when considering secular perturbative forces such as J2 and atmospheric drag. This work compares the performance of several di erent techniques for estimating mean di erential orbital elements of a controlled spacecraft in formation ight with a passive target. Inter-satellite range measurements, line of sight measurements, and single di erence pseudorange and carrier phase measurements from the Global Positioning System constellation are considered. An extended Kalman lter and a Gauss-Newton batch estimator are formulated for the di erent measurements and their performances evaluated when subjected to measurement noise. These estimators are compared on the basis of their nal estimation accuracy, and noise levels for inter-satellite range and LOS measurements to achieve precise formation ight are presented.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".