Extended Kalman filtering for pico-satellites attitude determination
Bibliographic record
Abstract
The extended Kalman filter (EKF) algorithm has been applied widely in orbital guidance and navigations problems for miniand micro-satellites. This paper evaluates the performance and computational complexity of EKF for smaller satellites. The impact of some limitations, including low power and computing capabilities on the implementation of EKF in smaller satellites is also addressed. The filter formulation is based on a kinematics model propagated with three-axis rate integrating gyros, where the attitude is parameterized using quaternions. A multiplicative quaternion-error approach is used to define the attitude error, which ensures that quaternion normalization is maintained. The results indicate that the EKF is sensitive to initial conditions. Using TRIAD algorithm to determine initial conditions, the filter converges after 50 minutes with an average error of 1.5°, but may diverge for extreme cases. The time and space complexities of EKF have also been estimated to be O(n3) and O(n2), respectively.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".