Acoustic tracking of a freely drifting sonobuoy field
Bibliographic record
Abstract
This paper develops an acoustic inversion algorithm to track a field of freely drifting sonobuoys using travel-time measurements from a series of nonsimultaneous impulsive sources deployed around the field. In this scenario, the time interval between sources can be sufficiently long that significant independent movement of the individual sonobuoys occurs. In addition, the source transmission instants are unknown, and the source positions and initial sonobuoy positions are known only approximately. The formulation developed here solves for the track of each sonobuoy (parametrized by the sonobuoy positions at the time of each source transmission), allowing arbitrary, independent sonobuoy motion between transmissions, as well as for the source positions and transmission instants. This leads to a strongly underdetermined inverse problem. However, regularized inversion provides meaningful solutions by incorporating a priori information consisting of prior estimates (with uncertainties) for the source positions and initial sonobuoy positions, and a physical model for preferred sonobuoy motion. Several models for sonobuoy motion are evaluated, with the best results obtained by minimizing the second spatial derivative of the tracks to obtain the minimum-curvature or smoothest track, subject to fitting the acoustic data to a statistically appropriate level.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".