Music-Enhanced CFAR for High Frequency Over-the-Horizon Radar
Bibliographic record
Abstract
To increase the number of location options for an HF surface-wave radar (HFSWR) there is significant interest in reducing the physical size of the receive array. Reducing the aperture results in a degradation of both sensitivity and azimuth information. Azimuth accuracy may be retained by the use of high-resolution methods (such as MUSIC) that have a significantly smaller beamwidth than standard beamforming. It is expected that the application of these high-resolution methods will help retain azimuth information with reduced aperture size. This paper evaluates the effects of reducing the physical aperture of the linear receive array used in HFSWR and using post-detection azimuth re-estimation by high-resolution methods to maintain azimuth resolution, accuracy, and hence tracking performance. This paper is limited to evaluating the effect of increased azimuth beamwidth and does not address the issue of reduced radar sensitivity. Data for the evaluation was obtained from an HFSWR system located at Cape Race, Newfoundland, Canada. The accuracy of the detection centroid for a full 16-element array is compared to the accuracy for a half-aperture 8-element array. It is shown that similar accuracy can be achieved from the shortened array employing the MUSIC-Enhanced CFAR compared to the full size array using the conventional CFAR processing.
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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 | 0.000 | 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".