Adaptive sonar detection performance prediction in an uncertain ocean
Bibliographic record
Abstract
This paper addresses the problem of predicting detection performance when the signal wavefront is uncertain and the noise field directionality is unknown. Passive sonar detection in this scenario typically involves robust adaptive beamforming with limited training data. The classical sonar equation, however, assumes the noise field and signal wavefront are known exactly. In this paper, we use the statistics of the generalized likelihood ratio test (GLRT) for the composite hypothesis of a multirank signal in Gaussian noise with unknown covariance matrix to evaluate the detection threshold (DT) as function of ocean uncertainty and number of noise training snapshots. Further, the trade-off between array gain (AG) and detection threshold (DT) is studied as a function of training sample size in a dynamic interference environment. Detection performance of the GLRT is characterized in terms of bounds on the middle 80th percentile of classical passive sonar figure of merit (FOM) and range-of-the-day (RD) over an ensemble of ocean environments. Different classes of environments including downward refracting and upward refracting scenarios are examined with particular attention to the Florida Straits region. Example performance prediction bounds are presented using real horizontal noise field and environmental data. [Work supported by ONR.]
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".