Multiple targets direction‐of‐arrival estimation in frequency scanning array antennas
Bibliographic record
Abstract
In this study, the authors address the problem of resolving angular position of multiple targets in the same range and separated by less than an antenna beamwidth using frequency scanning array (FSA) antennas. First, the frequency scanning antenna signal model is derived and then the necessary compensation methods to overcome antenna pattern variations with frequency during the scan in FSAs are presented. Two direction‐of‐arrival (DOA) estimation algorithms, the minimum variance beamforming and the maximum‐likelihood estimation are applied on the signal model. Simulation results show that both methods can separate targets with angular separations smaller than a beamwidth by selecting correct parameters. The performance of the two DOA estimation methods with respect to different system parameters are investigated based on the signal model through Monte Carlo simulations and compared with the Cramér–Rao lower bound. In addition, an FSA antenna is presented in this work and simulations of the DOA estimation algorithms are performed using the measured antenna pattern of this antenna. The performance and limitations of target DOA estimation methods for the measured antenna patterns are also discussed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".