Gyro Observability Research of a Relative Attitude Determination System Based on Stereo Vision
Bibliographic record
Abstract
The analysis of the observability of system states is very important in the design of an optimal filter estimation algorithm. A relative attitude estimation algorithm is developed based on a stereo vision system and a gyroscope, and the observability of this algorithm is studied. First, we build the error model of the relative attitude determination system. Second, the observability of every state of the filter is studied. Third, by choosing different variables as the states of the error model, the unobservable subspace of the system is confirmed. Furthermore, the system structural decomposition reveals that this type of relative attitude determination system can only determine the relative attitude between the deputy and the chief and that their gyro drift errors are unobservable. In addition, the structural decomposition also tells us that when the feature points measured by the stereo vision system are greater than two, increasing the number of feature points provides little benefit for improving the observability of the gyro drift errors. Considering the incomplete observability of the original system, the star sensor is added into the system to enable it to be completely observable. The final simulation result indicates that after adding the star sensor, the system, which becomes completely observable, can estimate the body attitude, the relative attitude and the gyro error while providing improved accuracy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".