Eigenvector-based test for local stationarity applied to beamforming
Bibliographic record
Abstract
Sonar experiments with large-aperture horizontal arrays often include a combination of targets moving at various speeds, resulting in non-stationary statistics of the data snapshots recorded at the array. Accurate estimation of the sample covariance (prior to beamforming and other array processing procedures) is achieved by including a large number of snapshots. In practice, this accuracy is affected by the requirement to limit the observation interval to snapshots with local stationarity. Data-driven statistical tests for stationarity are then relevant as they allow determining the maximum number of snapshots (i.e., the best case scenario) for sample covariance estimation. This work presents an eigenvector-based test for local stationarity. It can be applied to the improvement of beamforming when targets must be detected in the presence of loud-slow interferers in the water column. Given a set of (possibly) non-stationary snapshots, the proposed approach forms subsets of a few snapshots, which are used to estimate a sequence of sample covariances. Based on the structure of sample eigenvectors, the proposed test gives a probability measure of whether such consecutive sample covariances have been drawn from the same underlying statistics. The approach is demonstrated with simulated data using parameters from the Shallow Water Array Processing (SWAP) project.
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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 | 0.008 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".