Angular misalignment calibration method for ultra‐short baseline positioning system based on matrix decomposition
Bibliographic record
Abstract
For an ultra‐short baseline (USBL) positioning system, the angular misalignment between the acoustic array and attitude sensor will introduce overwhelming positioning errors. In order to eliminate this type of errors, a method to calibrate angular misalignment is proposed here. In the method, individual angular misalignment is estimated through the decomposition of the related rotating matrix and overall angular misalignment is calculated using an iterative estimator. Not only does the method determine the angular misalignment more accurately but also it can be applicable to any arbitrary trajectory even the pre‐determined trajectory is distorted by the environmental forces (such as winds, currents). The estimation error of the method is analysed through a simulation with different types of trajectories. Its performance is also evaluated in a field experiment. The method is compared with an existing method using both simulated and field data. The simulation and field experiment results indicate that the method has better performance and does improve the positioning accuracy of a USBL system.
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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 | 0.001 |
| 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 | 0.002 |
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".