A Multimodal Approach for Attitude Measurement of Near-Earth Daytime Star Sensors
Bibliographic record
Abstract
When star sensors operate under near-Earth daytime conditions, the intense background radiation from the sky severely interferes with the energy of the star points in the imagery, resulting in a low signal-to-noise ratio (SNR) for the star points.This low SNR hinders target extraction and centroid positioning, thereby affecting the normal attitude measurement of star sensors.Addressing the challenge of attitude measurement under daytime conditions, this study first analyzes the mapping relationship of pixel positions in consecutive frames of star sensor imagery.A star image superposition algorithm based on attitude-related frames is proposed.On this foundation, an attitude measurement method based on star image superposition is employed for measuring the attitude of daytime star sensors.Furthermore, a fitting algorithm for the solar centroid is introduced, and a coarse measurement method based on solar position is applied to determine the optical axis orientation of daytime star sensors, enhancing their robustness in daylight conditions.The algorithm proposed in this study is validated through experiments.The results demonstrate that the multimodal attitude measurement method not only effectively improves the SNR of near-Earth daytime star sensor imagery through star image superposition, ensuring the accuracy of attitude measurements, but also ensures the robustness of attitude measurements through solar centroid fitting.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".