Maneuvering target tracking from nautical radar images using particle-Kalman filters
Bibliographic record
Abstract
In this paper, a new combined particle-Kalman filter (PF-KF)-based visual tracking approach is designed for maneuvering target tracking from X-band nautical radar images. Unlike existing target tracking approaches used by nautical radar, this approach incorporates a histogram-based visual tracking strategy to estimate the target position and velocity. It applies a sampling importance resampling (SIR) particle filter to obtain preliminary target positions, and then a Kalman filter to derive refined target position and velocity. A Bhattacharyya coefficient-based similarity function is employed to compare the reference target and candidate target models, which are constructed by a kernel-based histogram in radar images. An enhanced reference target model construction method that employs constant false alarm rate (CFAR) processing to enable automatic determination of reference region is proposed to improve the tracking stability and accuracy. Comparison of the target information obtained by the proposed PF-KF method from various field X-band nautical radar image sequences with those measured by GPS shows the proposed approach can provide a reliable and flexible online target tracking for nautical radar application. It is also shown that, in the scenario of strong sea clutter, the proposed approach outperforms the PF-only-based approach and the classical tracking approach which combines order-statistics (OS) CFAR processing and the Kalman filter.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".