Sparse broadband beamformer design via proximal optimization Techniques
Bibliographic record
Abstract
Beamforming is one of the most important techniques to enhance the quality of signal in array sensor signal processing, and the performance of a beamformer is usually related to the design of array configuration and beamformer weight. Recently, it was realized that the sparsity of the filter coefficients can reduce the cost of signal acquisition and communication, and as a consequence, the sparse broadband beamformer design attracts more and more attentions. In this paper, we first propose a proximal sparse beamformer design model which obtains the sparse and robust filter coefficients through solving a composite optimization problem. The objective function of the model is the sum of a least squares term, a proximal term, and an 1 -regularization term. The least squares term reflects the data fidelity; the proximal term, whose center is predetermined via a simple least squares, enhances the robustness; while the 1 term ensures the sparsity of the solution. This model not only maintains the authenticity of the least squares solution, but also ensures the sparsity of the filter coefficients. A significant feature of the model is that we use 'partial' data to obtain the least squares solution and use another 'partial' data to construct the data fidelity term, which can evidently decrease the computational cost. For solving the composite optimization problem, we tailor several popular algorithms, such as the alternating direction method of multipliers, the forward-backward splitting method, and the Douglas-Rachford splitting method. Numerical results observably exhibit the improvements of the proposed approach over existing works in both effectiveness and efficiencies.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".