Satellite Angular Velocity Estimation Based on Optical Flow Technique
Bibliographic record
Abstract
This paper examines star tracker rate estimation using optical flow of two successive stars’ images. Modern star trackers are able to provide both attitude and rate estimates at high slew rates, in addition to typical stationary conditions. Current star detection methodologies are not robust for higher angular velocities because they segment star images. Speeded-up robust feature (SURF) algorithm is suitable for star images since it is robust to moderate slew rates because of its scaleand rotation-invariant feature detector and descriptor. In this paper, the SURF algorithm is implemented for star labeling and a variation of Random Sample Consensus (RANSAC) is applied to improve the results of SURF feature matching. After applying the optical flow algorithm on pixels of interest, we use a least squares optimization and a camera model to evaluate the spacecraft’s angular velocity. Since this procedure does not rely on inertial attitude measurements, it remains applicable even when star matching is not possible. The proposed algorithm is implemented on star tracker ST-16 and its performance is assessed by numerical simulation and star images generated by hardware in the loop laboratory testing. The simulation results show very accurate angular velocity estimation for slew rates of up to 10 degrees/s.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".