Bibliographic record
Abstract
In this work, we explore the performance of a new algorithm for the estimation of signal and noise subspaces from limited data collected by a large-aperture sonar array. Based on statistical properties of scalar products between deterministic and complex random vectors, the proposed algorithm defines a statistically justified threshold to identify target-related features (i.e., wavefronts) embedded in the sample eigenvectors. This leads to an improved estimator for the signal-bearing eigenspace that can be applied to known eigenspace beamforming processors. It is shown that data projection into the improved subspace allows better detection of closely spaced targets compared to current subspace beamformers, which utilize a subset of the unaltered sample eigenvectors for subspace estimation. In addition, the proposed threshold gives the user control over the maximum number of false detections by the beamformer. Simulated data are used to quantify the performance of the signal subspace estimator according to a normalized metric that compares estimated and true signal subspaces. Improvement on beamforming resolution using the proposed method is illustrated with simulated data corresponding to a horizontal line array, as well as experimental data from the Shallow Water Array Performance experiment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".