An Applied Method for Clustering Extended Targets With UHF Radar
Bibliographic record
Abstract
In this paper, the application of coherent ultra-high frequency (UHF) Doppler radar for ship target detection over river is investigated. Due to the wide beam and high resolution of UHF radar, ship target echoes are usually significantly extended in both the range and Doppler dimensions of the radar Range-Doppler (R-D) spectrum. The range and radial velocity of the extended target are difficult to be determined using a constant false alarm rate (CFAR) detector, especially for the low-radial-velocity case in which the detection performance of CFAR detector is deteriorated due to strong river clutter. To solve this problem, an applied clustering method is proposed to detect and classify multiple targets and obtain corresponding target centers from the CFAR outputs. The target extension characteristics, which are used for clustering, are modeled and employed in segments for different range. The effectiveness of the proposed method is validated using both simulated and field data and the clustering method can classify extended targets without the need of knowing the number of targets beforehand.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".