A simple suboptimal Kalman filter implementation for a gyro-corrected satellite attitude determination system
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Bibliographic record
Abstract
This article presents a simple Kalman filter implementation for correcting gyro-determined satellite attitude estimates with attitude measurements made using external sensors such as sun sensors, magnetometers, star trackers, and so on. This article first generalizes a recently developed non-linear observer for the gyro-corrected attitude determination problem. By implementing the steady-state Kalman filter in the framework of this non-linear observer, a computationally simple filter is obtained with suboptimal steady-state performance. This is important for applications where computational power is limited, such as in micro-/nano-satellite applications. Additionally, in the absence of process and measurement noise, this implementation of the Kalman filter is globally stable. The resulting filter uses constant steady-state Kalman filter gains. It is demonstrated that close-to-optimal steady-state performance is obtained.
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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