Motion and Parameter Estimation of Space Objects Using Laser-Vision Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
free-falling tumbling satellite (target). The filter receives only noisy pose measurements from a laser-vision system aboard another satellite (chaser) at a close distance in a neighboring orbit. The filter estimates the full states, all the inertia parameters of the target satellite, and the covariance of the measurement noise. A comprehensive dynamics model that includes aspects of orbital mechanics is incorporated for accurate estimation. The discrete-time model, whichinvolvesastate-transitionmatrixandthecovarianceofprocessnoise,isderivedinclosedform,thusrendering the filtersuitableforreal-timeimplementation.Thestatisticalcharacteristicsofthemeasurementnoiseisformulated by a state-dependent covariance matrix. This model allows additive quaternion noise, while preserving the unitnorm property of the quaternion. The convergence properties of the developed filter is demonstrated by simulation andexperimental results. These results also demonstrate that the filter can continuously produce accurate estimates of pose even when the vision system is occluded for tens of seconds.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 it