<title>Geometric approach to target tracking motion analysis in bearing-only tracking</title>
Bibliographic record
Abstract
In maritime operations, target tracking and localization, also called target motion analysis (TMA), is an important issue. If an active sensor is used, the tracking process will be observable since we can predict the target range and bearing without any difficulty. The major disadvantage of using the active sources is that the enemy's targets can easily detect the ship position. Thus, tracking using active sources become a risky proposition. The alternative is to use passive tracking, but in this case the tracking process will be unobservable because we can only measure the target bearing. The range can be estimated via triangularization by using at least two platforms. Another method is to try to find the range using a geometrical approach to have at least one accurate range and then we can use it to construct the track under some assumptions. In this paper, a geometrical approach to bearing-only tracking is introduced. The target range is derived using few bearing measurements. Several own ship-target geometries have been set up for this purpose. To compute the target range, it is required that the own ship execute an admissible maneuver. The geometrical approach presented provides an acceptable performance and can be used for a short time period in the tracking process to provide a reasonable estimate of the range and then the tracker can use this range to generate the target track and hence reduce the bias.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.018 | 0.011 |
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".