Improving transient performance in computationally simple gyro-corrected satellite attitude determination
Bibliographic record
Abstract
This article extends a previously presented Kalman-like filter for fusing gyro measurements with attitude measurements obtained from external sensors such as star-trackers, magnetometers, and sun-sensors. The simplicity of this approach is useful for applications such as micro/nano-satellites where computational power is limited. The previously presented filter uses constant filter gains. It was shown that by appropriately choosing the constant gains, near- optimal steady-state performance is obtained. A drawback of using constant gains is that optimal transient filter performance is lost, meaning that convergence is slower. In this article, a computationally simple analytical approximation to the transient part of the Kalman filter gain is derived, allowing the transient performance to be recovered. The transient approximation of the gain is then applied for a predetermined fixed period of time, after which the gain is switched to the previously presented constant steady-state Kalman gain. A simple expression for a suitable time to switch from transient to steady-state filter operation is derived. Simulation results demonstrate the efficacy of this approach.
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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".