Satellite Angular Velocity Estimation Based on Optical Flow Technique
Why this work is in the frame
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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.
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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