Test for eigenspace stationarity applied to multi-rate adaptive beamformer
Bibliographic record
Abstract
Array processing in the presence of moving targets is challenging since the number of stationary data snapshots required for estimation of the data covariance are limited. For experimental scenarios that include a combination of fast maneuvering loud interferers and quiet targets, the multi-rate adaptive beamformer (MRABF) can mitigate the effect of non-stationarity. In MRABF, the eigenspace associated to loud interferers is first estimated and removed, followed by application of adaptive beamforming techniques to the remaining, less variable, “target” subspace. Selection of the number of snapshots used for estimation of the interferer eigenspace is crucial in the operation of MRABF, since too few snapshots result in poor eigenspace estimation, while too many snapshots result in leakage of non-stationary interferer effects into the target subspace. In this work an eigenvector-based test for data stationarity, recently developed in the context of very large arrays with snapshot deficiency, is used as a quantitative method to select the optimal number of snapshots for the estimation of the non-stationary eigenspace. The approach is demonstrated with simulated and experimental data from the Shallow Water Array Performance (SWAP) 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 | 0.005 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".