A Novel Multi-Dimensional Spectrum Estimation Technique using Antenna Array Displacement
Bibliographic record
Abstract
Conventionally, in multi-dimensional spectral estimation techniques, each data snapshot in space is captured simultaneously. All antenna elements or sensors being used to collect data are sampled at the same time. By doing so, the size of the antenna array is proportional to the area of interest in space. The antenna array is prohibitively huge if the area that we want to cover is large. In this paper, in order to reduce the number of antenna elements in use, we propose a novel multi-dimensional spectrum estimation technique based on displacing small antenna arrays along predefined paths. It includes a data measurement technique which sequentially collects data samples within each snapshot in space according to a predefined order, and a spectral estimation technique which is based on the Discrete Fourier Transform (DFT) of the collected data. The key idea is to create a large synthetic antenna aperture by displacing a small antenna array along a predefined trajectory. Impinging waves are assumed uniform plane waves. The performance of the proposed technique is evaluated by simulation. The applications of the proposed technique include synthetic aperture radar, radar image processing and sonar systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".