Sensor array beamforming using random channel sampling: The aggregate beamformer
Bibliographic record
Abstract
The aggregate (AGG) beamformer can provide significant savings in hardware and software cost and complexity relative to conventional beamformers while retaining equivalent directional performance. The key to achieving the savings is collecting data from array channels in a random sequence, rather than simultaneously or sequentially. This allows the AGG beamformer to convert unwanted off-beam signals into wideband noise that can then be reduced by filtering. The total sampling rate is chosen to obtain the desired residual noise level and signal bandwidth. It is independent of the number of sensors. Analog anti-alias filters normally are not required, since the Nyquist frequency is determined by the total sampling rate, not the (lower) per-channel sampling rate. Beamformer delay quantization and steering resolution are greatly improved relative to conventional beamforming without the need for data interpolation. The beamformed signal, prior to decimation filtering, is obtained without arithmetic operations on the data. Low front-end hardware-complexity makes the AGG beamformer suitable for highly integrated systems. The number of sensors in the array can be altered without re-configuring buffers or altering the sampling rate. The principles of the AGG beamformer are introduced, and simulation and experimental results demonstrate performance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.002 |
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".