A Joint Detection and Tracking Paradigm Based on Reinforcement Learning for Compact HFSWR
Bibliographic record
Abstract
Due to its limited transmit power and smaller receiving antenna array, compact high-frequency surface wave radar often encounters increased challenges in detecting and tracking sea-surface targets continuously. In tracking scenarios with dense clutter or multiple targets, weak target signals are often missed due to improper detection thresholds, leading to track fragmentations during target tracking. In order to improve target detection probability and enhance target tracking continuity, a joint detection and tracking (JDT) paradigm, which establishes a closed loop between the detector and tracker, is proposed. When a target of interest is tracked, the tracker sends its predicted range, Doppler velocity, and azimuth back to the detector, then the detector builds a detection gate centered at the predicted range and Doppler velocity on the range–Doppler map. Within the detection gate, an optimal detection threshold dependent on the detection background and tracking environment is determined using reinforcement learning. In this way, a potential target plot may be detected with a higher detection probability and the detected plot is provided for track update. The proposed paradigm employs tracking information to provide adaptive detection parameters for specific targets through reinforcement learning to enhance both target detection and tracking performance. Experimental results with field data demonstrate that compared with traditional detection before tracking scheme, the proposed JDT paradigm achieves a superior performance with the average tracking time on target being increased by 13.33 min and the average missed detection rate being reduced by 0.8$\%$.
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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 | 0.001 | 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".