Improved torpedo range estimation using modified fast orthogonal search techniques
Bibliographic record
Abstract
This work introduces a modified torpedo detection algorithm (MTDA) that improves upon the range estimates of an earlier torpedo detection algorithm (TDA). The original TDA detects the presence of a direct path and a surface reflected path for a torpedo acoustic tonal using the fast orthogonal search (FOS) algorithm. In the original TDA the candidate functions used by FOS were sinusoidal functions at a constant frequency. Using the frequencies of the direct and reflected path signal, the TDA estimated the torpedo range. It is known that the frequency of the direct path and reflect path signal will vary in time. It is also well known that correlating a received signal with the expected signal results in the lowest probability of error in detection (matched filter). Thus in this work the candidate functions used by FOS are functions whose frequencies vary in time (chirp signals) as theoretically expected for the direct and reflected path signals. Also, the FOS algorithm is modified to fit the direct and reflected paths in pairs. The pair of frequencies that fit the highest energy is determined to be the direct and reflected path signal and the range used to generate that candidate pair is used as the range estimate. The MTDA algorithm is simulated for a torpedo approaching an receiver at several angles and the range estimations are shown. These results are compared with the earlier TDA and shown to be significantly improved.
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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 | 0.004 |
| 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 | 0.002 |
| 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".