A COMPUTATIONALLY EFFICIENT APPROACH FOR TARGET TRACKING USING RECENT FINITE OBSERVATIONS
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a computationally efficient approach for the target tracking of two-dimensional target systems with unknown maneuvers. To describe efficiently target with maneuvers, only two target models are developed. To estimate target trajectories and unknown maneuvers, two tracking filters using only the most recent finite observations are used due to fast estimation performance and low computation burden. It will be shown that constant-bias maneuvers do not induce the error in estimates of target trajectories. A detection method is also developed in order to indicate the presence of maneuvers and select the valid estimate from two filters. It is shown that the on-line computation amount of the proposed approach is much less than that of the well-known interacting multiple model (IMM) approach. Via extensive simulations, the performance of the proposed target tracking approach is evaluated by the comparison with existing IMM approaches.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it