Non-damping system reset algorithm for shipborne grid strap-down inertial navigation systems
Bibliographic record
Abstract
Abstract The system reset algorithms realized in damping state rely heavily on the output of Doppler velocity logs (DVLs). Aiming at a shipborne grid strap-down inertial navigation system (SGSINS), this paper addresses a non-damping system reset algorithm to estimate the gyroscope drifts. First, the SGSINS is integrated with the DVL to estimate the horizontal attitude errors, and the estimation results are introduced to the system reset. Next, to ensure the equation can be utilized to design the non-damping system reset scheme, the equation is reformulated by reserving the horizontal attitude errors as the correction terms. Finally, with the assistance of two intermittent or short continuous external yaw and position, the two-point and optimum system reset schemes are designed based on the equation and equation. Simulation results indicate the algorithm can estimate the gyroscope drifts accurately in non-damping state. Compensating the gyroscope drifts can efficiently suppress the drifted errors in SGSINS. Because it takes a short time to estimate the horizontal attitude errors, compared with the existing algorithms realized in damping state, the proposed algorithm greatly shortens the time of using DVL, and this improves the reliability of the algorithm in practical application.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".