A Fuzzy Logic Framework to Estimate the Angular Turn Rate for High-Performance Target Tracking
Why this work is in the frame
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Bibliographic record
Abstract
Most tracking algorithms are model based because knowledge of target motion is available. A successful target tracking depends on the choice of a good model of the target; which facilitates the extraction of useful information about the target's state from observations. A good model-based tracking algorithm will outperform any model-free tracking algorithm to a great extent if the underlying model turns out to be a good one. In this paper, we propose a fuzzy logic framework to expect the angular turn rate and, thus, the appropriate model for target tracking using the particle filter. The coordinate turn (CT) model with unknown turn rate is used to calculate the expected angular turn rate. The fuzzy logic system is comprised of double-input single-output; which is presented by fuzzy relational equations. A canonical-rule based form is used to express each of these fuzzy relational equations. The dynamics of the high-performance target are modeled by multiple switching (jump Markov) systems. The proposed algorithm showed better performance in tracking a maneuvering target in relative slow scan periods compared to fuzzy integrated multiple models (FIMM).
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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