High-precision measurement of attitude rotation period for non-cooperative space target based on spectral features
Bibliographic record
Abstract
The positioning of non-cooperative targets in space and the estimation of their parameters have become pivotal technologies for on-orbit service missions. Indeed, the "spectral fingerprint" of the target serves as a crucial tool for estimating the attitude-rotation period of non-cooperative targets. However, the spectral characteristics of the target show significant variation with changes in the target's attitude. Mitigating the impact of irradiance from the observation angle is a critical factor in developing a high-precision non-cooperative spatial target attitude rotation period estimation algorithm based on spectral characteristics. To address this challenge, this project employs a systematic research approach to investigate the prediction of non-cooperative target attitudes based on target spectra. Additionally, it conducts experimental studies to examine the impact of satellite rotation period identification in the presence of nutation. The research findings indicate that spectral features and shape projection features are effective in identifying the satellite attitude state. In the analysis of random experiments, an average recognition rate of 97.9 percent was achieved across different satellite tilt angles and rotation axes. Furthermore, the relationship between the angle of tilt and the observation axis had a negligible effect on period recognition. The incorporation of nutation angles did not affect the precision of the satellite rotation period estimation. However, the accuracy of this estimation varied depending on the satellite’s solar sail rotational state, ranging from 78% to 87%. In this study, an algorithm for estimating the attitude-rotation period of non-cooperative targets using target spectral and shape projection features was established, thereby providing a technical foundation for the position measurement and state estimation of non-cooperative targets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".