Passive Underwater Event and Object Detection Based on Time Difference of Arrival
Bibliographic record
Abstract
Underwater event/object detection is an enabling technique for many marine applications. for the surveillance of target water areas, the future underwater network can serve as a backbone system, and every sensor in this network is an agent. When the target moves into the target area or when an event happens, the agents will detect acoustic signals from the target or event. The acoustic waves arrive at different agents at different time. Based on the correlation of the received signals between two agents, the time difference of arrival (TDoA) can be estimated, which locks the target/event’s position on one branch of a hyperbola, represented by a nonlinear equation. With three or more agents, the target/event’s position can be uniquely decided. To make this system universally applicable, the average underwater acoustic velocity is also assumed to be unavailable, and a two-phase linear algorithm is proposed. A coarse estimation is obtained in Phase I, and the result is further refined in the Phase II. Extensive simulations are provided to verify the effectiveness of the proposed 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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 0.001 |
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".