A 3-D spatially-FIR RF frustum digital filter with microwave channelization for FPAs
Bibliographic record
Abstract
Focal plane arrays (FPAs) find applications in dish-antenna based receivers having multiple wideband beams. Such beams have a plurality of off-axes directions from the main beam. Maximally-decimated multirate 3-D digital FIR frustum filterbank can be employed in an FPA receiver to reduce directional jamming/clutter and other interference, noise, mutual electromagnetic coupling effects, and out-of-system noise/interference from warm bodies close to the antenna. A 3-D frustum filter requires high arithmetic complexity in the DSP hardware and sophisticated wide-band RF-front ends featuring high dynamic range. The temporal channelization of FPA elements using microwave triplexers achieves frequency channelization in continuous-time. Channelization is moved before the LNA stage, thereby allowing considerable improvement in dynamic range requirements for the RF-front end. The channelization reduces LNA bandwidth for each channel. The signals within the microwave channelizer subbands can either be down-converted through mixers, or directly sub-sampled by means of bandpass sampling A/D converters. Integration of multi-dimensional signal processing theory - which, traditionally is built around DSP methods - with microwave engineering, yields mixed-mode microwave-digital realizations of 3-D frustum filters having applications in FPA receivers. Simulation results of 3-D microwave-digital FIR mixed-domain three-channel frustum filters show SNR improvement of 21.5 dB and SIR improvement for 24.2 dB, for wideband applications in the range 1-4 GHz using an FPA having 32 × 32 elements with a dish of aperture 5m and focal length 7m.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".