High-resolution B-scan bearing estimation using the fast orthogonal search
Bibliographic record
Abstract
A directional DIFAR passive sonobuoy has an omni-directional channel and two orthogonal directional channels. Acoustic targets can be detected on a plot of the power-spectral density of the omni-directional channel called a LOFARGRAM. In addition, a bearing estimate can be formed by the arctangent of the sine channel over the cosine channel (B-scan estimate). Traditionally, the fast-Fourier transform has been used to perform the estimate of the power-spectral density. The fast orthogonal search (FOS) algorithm has been to shown give a higher resolution spectral estimate than the FFT algorithm. The power-spectral estimate of the FOS algorithm has been used instead of the FFT in generating the LOFARGRAM and B-scan bearing estimates. It is shown in simulation and experimental data that using the FOS algorithm allows two targets, whose frequencies are spaced closer than the FFT resolution, to be resolved. By resolving two targets, two bearing estimates are calculated and the resulting estimates are much more accurate than the FFT based B-scan bearing estimates.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".