Broadband source localization using matched correlation processing
Bibliographic record
Abstract
For many years, model-based signal processing algorithms using Matched Field Processing (MFP) techniques have been analyzed with the goal of improving the capability of passive sonar systems for localizing quiet underwater sources. Recently, researchers at DRDC Atlantic have been investigating Matched Correlation Processing (MCP) as a faster alternative to MFP. In this method, the cross-correlations for a source as measured with a pair of hydrophones in a horizontal array are matched with those generated with a correlation model for many candidate ranges and depths along a candidate bearing. These matches are carried out with a number of hydrophone pairs to form many ambiguity surfaces. The maximum on the average of these surfaces is assumed to yield the best estimate of the source position. By carrying out this procedure over a number of candidate bearings, a full 3-D search for the source location is achieved. Since 2002, a number of localization trials have been carried out east of Nova Scotia, Canada. During those trials, an array was deployed on the sea floor and used to collect acoustic signals from various broadband sources. In this paper, we describe the broadband MCP localization technique and show some localization results from those trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".