A Parameterized Geometric Magnetic Field Calibration Method for Vehicles with Moving Masses with Applications to Underwater Gliders
Bibliographic record
Abstract
The accuracy of magnetic measurements performed by autonomous vehicles is often limited by the presence of moving ferrous masses. This work presents a parameterized ellipsoid field calibration method for magnetic measurements in the sensor frame. In this manner, the ellipsoidal calibration coefficients are dependent on the locations of the moving masses. The parameterized calibration method is evaluated through field trials with an autonomous underwater glider equipped with a low power precision fluxgate sensor. A first set of field trials were performed in the East Arm of Bonne Bay, Newfoundland, in December 2013. During these trials, a series of calibration profiles with the mass shifting and ballast mechanisms at different locations were performed before and after the survey portion of the trials. Further trials were performed in the Labrador Sea in July 2014 with two reduced sets of calibration runs. The nominal ellipsoidal coefficients were extracted using the full set of measurements from a set of calibration profiles and used as the initial conditions for the polynomials, which define each parameterized coefficient. These polynomials as well as the sensor misalignment matrix were then optimized using a gradient descent solver, which minimizes both the total magnetic field difference and the vertical magnetic field variance between the modeled and measured values. Including the vertical field in this manner allows for convergence in spite of severe limitations on the platform's motion and for computation of the vehicle's magnetic heading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".