Concurrent adaptive beamforming for standard hexagonal array based on dual norm‐constraint correntropy in the presence of alpha stable noise
Bibliographic record
Abstract
This article presents dual norm-constraint correntropy beamforming methods for standard hexagonal array (SHA) to mitigate the effects of alpha stable noise and maintain the sparsity of filter coefficients. Both goals are achieved simultaneously through the use of norm regularization constraint and the novel convex combination technique. Firstly, we construct constrained optimization equations considered the constraints present in the constrained least-mean-square (CLMS) algorithm by incorporating the maximum correntropy criterion (MCC). In addition, in order to decrease the number of active elements for limited power supply array system, we introduce an L1-norm equation to the list of constraints of the adaptive filter that forces the coefficients with small magnitudes to zero. Then, we utilize the convexity and stability of L2-norm to devise the constrained maximum correntropy gradient L2-norm (CMCG-L2) for further reducing the misadjustment caused by alpha noise and improving the directivity performance of the adaptive beamformer. A novel convex combination scheme is also reported to satisfy the conflicting requirements between the sparsity and mean-square-error. Our simulation results demonstrate the superiority of the proposed methods over other previously developed beamforming techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".