Patch and monopole antennas in linear coprime arrays for direction of arrival estimation using compressed sensing
Bibliographic record
Abstract
Original work on direction of arrival (DOA) estimation relied on uniform linear arrays (ULAs) of antennas. Most of the work focused on improving the algorithms and the configuration of the antenna array and overlooked the effects of practical antennas on the algorithm performance. Very limited work studied DOA estimation within the physical limitations of handheld devices. In this work, we introduce three nonuniform linear coprime arrays based on patch and monopole antenna elements operating in 2.1 and 5.8 GHz bands and assess their behaviour in DOA estimation. The complex radiation patterns of the arrays were incorporated in the DOA estimation algorithm using compressed sensing (CS). Estimation accuracy is quantified by the root mean square error (RMSE) and the results are compared with those obtained by using isotropic antennas, showing that physical antennas can introduce up to 8° of error. Simulations were also carried out using the multiple signal classification (MUSIC) algorithm to demonstrate the advantage of CS in coprime arrays. The MUSIC algorithm failed to detect all sources even at maximum SNR. The impact of reducing the fundamental inter‐element spacing in coprime arrays below to achieve smaller array sizes is investigated as well.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".