Multidimensional-DSP Beamformers Using the ROACH-2 FPGA Platform
Bibliographic record
Abstract
Antenna array-based multi-dimensional infinite-impulse response (IIR) digital beamformers are employed in a multitude of radio frequency (RF) applications ranging from electronically-scanned radar, radio telescopes, long-range detection and target tracking. A method to design 3D IIR beam filters using 2D IIR beam filters is described. A cascaded 2D IIR beam filter architecture is proposed based on systolic array architecture as an alternative for an existing radar application. Differential-form transfer function and polyphase structures are employed in the design to gain an increase in the speed of operation to gigahertz range. The feasibility of practical implementation of a 4-phase polyphase 2D IIR beam filter is explored. A digital hardware prototype is designed, implemented and tested using a ROACH-2 Field Programmable Gate Array (FPGA) platform fitted with a Xilinx Virtex-6 SX475T FPGA chip and multi-input analog-to-digital converters (ADC) boards set to a maximum sampling rate of 960 MHz. The article describes a method to build a 3D IIR beamformer using polyphase structures. A comparison of technical specifications of an existing radar application based on phased-array and the proposed 3D IIR beamformer is also explained to illustrate the proposed method to be a better alternative for such applications.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".