Reinforcement Learning Based Joint Detection and Tracking of Target for Compact HFSWR
Bibliographic record
Abstract
Compact high-frequency surface wave radar (HF-SWR) suffers from a low signal-to-noise ratio and low target detection probability, leading to track fragmentations during target tracking. To improve the target tracking continuity, a joint detection and tracking framework based on reinforcement learning (RL) is proposed. First, the interaction between the detector and tracker is established and a local range-Doppler (R-D) region where a target of interest may be located is extracted according to the predicted target state provided by the tracker. Second, the detector, as an agent of RL, perceives the detection background within the local R-D region and the track update status of the target. Finally, optimal detection thresholds in this local R-D region are determined to adapt to the current environment, and candidate target plots can be generated and provided to the tracker for target tracking. Experimental results demonstrate that the proposed method improves the detection probability of compact HFSWR significantly and track fragmentations caused by missed detections are greatly reduced.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".