Multistatic performance prediction for Doppler-sensitive waveforms in a shallow-water environment
Bibliographic record
Abstract
Navies worldwide are now operationally capable of exploiting multistatic sonar technology. One of the purported advantages of multistatics when detecting directional targets should be the increased probability of receiving a strong reflection at one of the multistatic receivers. However, it is not yet clear (or intuitive) how best to deploy multistatic-capable assets to achieve particular mission objectives. The Performance Assessment for Tactical Systems (PATS) software was recently developed by Maritime Way Scientific under contract to Defence Research and Development Canada as a research tool to assist in exploring different approaches to multistatic performance modelling. Beginning with a user-defined environment and sensor layout, PATS uses transmission loss and reverberation model results to calculate signal excess at each grid point in the model domain. Monte Carlo simulations using many realizations of target tracks allow for the calculation of the cumulative probability of detection as a means to assess performance. Results will be presented comparing the shallow-water performance of monostatic and multistatic sensors using frequency-modulated and Doppler-sensitive waveforms as well as omnidirectional and directional targets in a variety of realistic military scenarios.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".