Enhanced Single-Snapshot 1-D and 2-D DOA Estimation Using Particle Swarm Optimization
Bibliographic record
Abstract
In radar applications, target range, velocity (Doppler), and angle are the three primary measurements employed.Estimating the number of targets and their directions of arrival (DOAs) on the antenna array can be achieved through various methods.Conventional techniques such as the correlation and Multiple Signal Classification (MUSIC) algorithms offer a straightforward approach for DOA estimation.However, these methods necessitate an exhaustive search of the entire spectrum and require numerous temporal snapshots to accurately identify the spatial spectrum peaks.To address this challenge, Particle Swarm Optimization (PSO)-correlation and PSO-MUSIC methods have been proposed.These PSO-based techniques provide a systematic approach to locate the spatial spectrum peak in both one-dimensional (1-D) and two-dimensional (2-D) scenarios.In order to determine the exact target position, the global best particle location is iteratively updated by these methods.The statistical performance of the 1-D and 2-D PSO-correlation and PSO-MUSIC algorithms demonstrates that these techniques exhibit higher accuracy in comparison to existing single-snapshot DOA estimation methods.The estimation performance of the proposed algorithms is analyzed and justified by employing the Cramé r-Rao bound (CRB).
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".