A New Satellite Attitude State Estimation Algorithm Using Quaternion Neural Networks
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Bibliographic record
Abstract
Satellite attitude control based on state feedback techniques requires measurement of all the state variables describing the attitude dynamics. The Extended Kalman Filter (EKF) has been used for this task, and works quite well in the general cases. However, the EKF is computationally intensive and requires a significant design effort due to mathematical modeling, linearization and its quaternion-motion version requires the use of two different attitude models a . A number of estimation techniques based on neural networks have shown to be more accurate than the EKF, but none of them seem to have been applied to satellite attitude or to quaternion motion. In this paper, a neural networks based satellite attitude estimation algorithm is presented. The proposed approach is original by using a quaternion neural network. It also presents a new way of integrating the neural network into the state estimator and develops a training procedure which is easy to implement. The suggested algorithm is shown to provide the same accuracy as the EKF with significantly lower computational complexity.
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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 | 0.001 |
| 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 it