Comparison of two detection combination algorithms for phased array radars
Bibliographic record
Abstract
Phased array radars have been widely studied. One issue observed is that adjacent radar beams detect the same target. This multiplicity is resulted from a few factors such as the radar beam spacing, radar power, target size and trajectory etc. It degrades the radar performance greatly by asking for redundant confirmation beams and therefore increasing the false track rate. No public solutions to detection combination have been reported. This paper provides a comparison of two straight forward detection combination algorithms: cross-line combination and in-line combination. The raw multiple detection data were generated by a simulator of multi-function radar (MFR) and the combination algorithms are evaluated with the recorded simulation data. With the given radar setup, the cross-line combination algorithm needs to buffer 2-3 scanned lines of data and the delay is about 2-3 seconds. The in-line combination algorithm reduces the buffer to one scanned line of data and its delay is about 1 second. However, the first algorithm is able to remove about 2/3 of raw detections and achieve a better performance of noise suppression. The later can reduce about 1/3 of the raw detection, with less noise suppression.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".