Sparse DOA Estimation for Directional Antenna Arrays: An Experimental Validation
Bibliographic record
Abstract
In the literature, antenna arrays for direction of arrival (DOA) estimation are being theoretically optimized to maximize the number of sources that can be estimated for a given number of isotropic elements. In this work, coprime arrays with monopole and patch elements are designed and fabricated to evaluate the impact of antenna directivity and realistic antenna behavior on DOA estimation accuracy. Two DOA estimation algorithms, namely: MUSIC and Lasso are applied where the complex radiation patterns are incorporated within the steering matrix and then the received signal is modified accordingly. With four sources, a root mean square error (RMSE) of around 4 and 1 degrees is achieved at 5 dB with monopole and patch elements, respectively. Furthermore, a software-defined radio (SDR) platform is utilized to experimentally evaluate the real-time performance in realistic environments. It is shown that when the DOA deviates from the boresight, the estimation error increases due to antenna directivity. The performance of the patch array is better than its monopole counterpart due to the inherent multipath mitigation in the directive antennas and polarization purity of the two radiated electric filed components. Experimental results show that the RMSE in the DOA estimation of two sources using coprime array with monopole and patch elements is around 4 and 1 degrees, respectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".