Torpedo detection using mutli-path signals and fast orthogonal search techniques
Bibliographic record
Abstract
Detecting a high speed torpedo by means of a passive acoustic detector is very challenging for most acoustic operators. Coupled with a very noisy environment, multiple sources in a multi-path scenario and varying environmental factors, a time-constrained assessment will prove difficult. In addition, a passive sensor cannot estimate the range of a torpedo approaching it at a constant bearing. The passive acoustic sensor will receive a direct path signal from the torpedo as well as a signal that has reflected off the surface. Due to the different angle of arrival, the direct-path and surface-reflected signals have different Doppler shifts. The Torpedo Detection Algorithm (TDA) employs the fast orthogonal search (FOS) algorithm for high-resolution spectral analysis to detect the closely spaced direct-path and surface-reflection signals. When a direct-path and surface-reflection are found, an automatic alert of a torpedo detection is initiated. In simulation, a torpedo is detected 20 times out of 20 as it travels from 5000 to 500 m from the receiver. Simple trigonometric expressions are used to estimate the torpedos range given the two frequencies estimated by FOS and a priori information about the torpedo speed and depth.
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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 | 0.000 | 0.001 |
| 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 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".