Adaptive Target Track Fusion Based on Track Quality Assessment for T/R - R Composite Compact HFSWR
Bibliographic record
Abstract
Compact high-frequency surface wave radar (HF-SWR) suffers from a low azimuth accuracy for target detection, leading to a low target positioning accuracy. Either monostatic T/R radar or bistatic T-R radar is difficult to accurately estimate the target kinematic parameters. To resolve this problem, an adaptive track fusion method based on track quality assessment is proposed in this paper. First, two indexes are proposed according to the vessel motion characteristics to evaluate the overall track quality in terms of uncertainty and smoothness. Subsequently, the two indexes are integrated to produce fusion weights for the target range and azimuth, respectively. The obtained weights are used to weight the range and azimuth data sequences measured by two radars to obtain a preliminary fused track. Next, the instantaneous course data sequences for the preliminary fused track and two radar tracks are separately estimated, and the course difference data sequences between the preliminary fused track and each radar track are obtained. Then a dynamic weight data sequence is calculated using the course difference data sequence. Finally, the obtained dynamic weights are used to weigh the range and azimuth data sequences of two radar tracks to produce the final fused track. Experiment results with both simulated and field data demonstrate that the proposed method improves the target positioning accuracy significantly.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".