Robust Minimum Variance Beamforming with Dual Response Constraints
Bibliographic record
Abstract
The minimum variance distortionless response (MVDR) beamformer is a popular method of combining multiple antenna outputs in order to recover a signal of interest (SOI) with known steering vector in the presence of noise and interference. However, in practice the precise value of the SOI steering vector is unknown and only an estimate is used. In hopes of protecting the actual SOI in case of mismatches it has been recently proposed to use a non-attenuation constraint inside a hypersphere centered at the presumed SOI steering vector. In an effort to strike a balance between robustness to steering vector error and interference-plus-noise suppression, we propose in this paper to use two concentric hyperspheres instead of one with different degrees of protection in each. We determine conditions on the user defined parameters to ensure existence of a solution to the resulting constrained optimization problem. The multiply constrained filter solution is demonstrated to be of the diagonally loaded type with adaptive loading factor. We further give necessary conditions for the two-constraint filter to be distinct, in terms of SINR, to the one-constraint case. Numerical simulations show that using two constraints yields improved SINR performance compared to one constraint for small steering vector mismatches or large input SNR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".