Bridging the Domain Gap Between Target Detection and Parameter Estimation: A Time-Frequency Parameter Estimation Scheme for HFSWR
Bibliographic record
Abstract
Time-frequency analysis (TFA) serves as an effective technique in improving target-monitoring performance of high frequency surface wave radar (HFSWR). However, the potential of this technique has not been fully explored in existing schemes because the extracted time-frequency (TF) signatures are solely used for identifying targets. In addition to missed detection, target non-stationarity may also degrade parameter estimation accuracy. To address this challenge, we propose a TF-based parameter estimation scheme. Specifically, we develop range and direction of arrival (DOA) methods that directly leverage TF signatures obtained during the detection stage to extract target parameters. These parameters are then processed for subsequent plot association and target localization stages. Statistical results on measured data show that the proposed range and DOA estimation methods outperform their conventional counterparts in accuracy. Next, these methods are integrated into the proposed parameter estimation scheme, which is then compared against existing schemes. Experimental results demonstrate that our scheme achieves better plot association performance compared with other conventional schemes. In addition, using automatic identification system (AIS) records as ground truth, our scheme achieves enhanced localization accuracy. Particularly, it reduces the proportion of anomalous coordinate trajectories by 2.05∼4.11%. Moreover, by seamlessly connecting target detection and parameter estimation stages within the TF domain, this scheme streamlines the overall target-monitoring pipeline.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".