Multiemitter Two-Dimensional Angle-of-Arrival Estimator via Compressive Sensing
Bibliographic record
Abstract
This article introduces a multiemitter 2-D angle-of-arrival (AoA) estimation scheme. It consists of an AoA estimation algorithm based on impinging signal spatial sparsity, Dantzig selector (an l1-norm minimization solution), and a six-element nonuniformly random-spaced 2-D array. The new scheme can identify more signals than the number of sensors without requiring a priori knowledge of signals and in a low signal-to-noise ratio (SNR) environment. It is demonstrated from 2 to 18 GHz with ten simultaneous incoming signals within 500 MHz instantaneous bandwidth. The number of signals, bandwidth, and frequency range are determined by the performance of digital receivers and are not limited by the compressive sensing (CS)-based 2D-AoA estimation scheme. Note that the random selection of element locations plays a vital role in ensuring that the new scheme achieves a highly successful estimation rate (SER). This is because random sensing is one of the key factors in CS. The only constraint of element locations is the spacing between elements, which has to be bigger than the diameter of cavity-backed-spiral-antenna. Simulation results show that the 2D-AoA estimator has more than 69%, 82%, and 90% SER if the measurement error is less than or equal to 1°, 2°, and 4°, respectively, when SNR is between -10 and 10 dB.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".