Modelling uncertainty in an underwater acoustic positioning system
Bibliographic record
Abstract
This paper develops a ray-based travel-time inversion to simulate the accuracy of an active underwater acoustic localization system, and examines the localization accuracy as a function of various sources of error and geometric and environmental factors.The system considered here simulates localizing an autonomous underwater vehicle using arrival times of acoustic transmissions from an onboard source as measured at hydrophones distributed spatially over a test range.Since localization uncertainty is a function of source location, uncertainties are calculated for the source at a grid of locations over the areas of the test bed.Localization accuracy is considered as a function of timing errors, uncertainty in hydrophone locations, target depth, variations in sound-speed profile, and hydrophone geometry. s o m m a i r eCet article dveloppe un inversion de temps d'arrive en traant des rayons pour simuler la prcision d'un systme actif de localisation acoustiques sous-marins, et examine la prcision de localisation en fonction de diverses sources d'erreur et de facteurs gomtriques et environnementale.Le systme considr ici simule la localisation d'un vhicule autonome sous-marin en utilisant les instants d'arrivs des transmissions acoustiques provenant d'une source bord tel que mesur partir d 'hydrophones rpartis spatialement sur une plage de test.Puisque l'incertitude de localisation est fonction de l'emplacement de la source, les incertitudes sont calcules pour la source une grille de lieux sur les zones du banc d'essai.La prcision de localisation est considre comme une fonction de synchronisation des erreurs, l'incertitude dans l 'emplacement des hydrophones, la profondeur des cibles, les variations du profile vitesse-son, et la gomtrie des hydrophones. Discover new heights in acoustics design
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".