Robust adaptive beamforming via estimating steering vector based on semidefinite relaxation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most of the known robust adaptive beamforming techniques can be unified under one framework. This is to use minimum variance distortionless response principle for beamforming vector computation in tandem with sample covariance matrix estimation and steering vector estimation based on some information about steering vector prior. Motivated by such unified framework, we develop a new robust adaptive beamforming method based on finding a more accurate estimate of the actual steering vector than the available prior. The objective for finding such steering vector estimate is the maximization of the beamformer output power under the constraints that the estimate does not converge to an interference steering vector and does not change the norm of the prior. The resulting optimization problem is a non-convex quadratically constrained quadratic programming problem, which is NP hard in general, but can be efficiently and exactly solved in our specific case. Our simulation results demonstrate the superiority of the proposed method over other robust adaptive beamforming methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it