Comprehensive calibration algorithm for long-endurance shipborne grid SINS
Bibliographic record
Abstract
Abstract A comprehensive calibration algorithm is proposed in this paper with the aim of solving the problem whereby the navigation error of a long-endurance shipborne grid strapdown inertial navigation system (SINS) drifts with time. First, the equation within the inertial frame is deduced to establish the relationship between the position error, the grid yaw error and the platform drift angle in the inertial frame; then in combination with the equation within the inertial frame, the calibration schemes are designed. The gyroscope drift in the body frame is estimated and compensated through the designed calibration schemes with the aid of information either on two intermittent external positions and yaw, or on three intermittent external positions. The simulation results illustrate that, in the former case, the three-axis gyroscope drifts can be estimated accurately; in the latter case, the z -axis gyroscope drift and the grid yaw error can be estimated accurately. Resetting the system error based on external navigation information and compensating the gyroscope drift can effectively restrain the accumulated navigation error of shipborne grid SINS; meanwhile the proposed algorithm is unaffected by the motion of the ship, which has significant practical value in engineering.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".