<title>Fuzzy-logic-based multitarget tracker</title>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The problem of multisensor-multitarget tracking is mainly dependent on the data association. In this paper, the fuzzy logic-based single target tracker is extended to the multitarget case. Multitarget scenario incorporating four targets both maneuvering and non-maneuvering in the same surveillance volume is analyzed. The proposed multitarget tracker, also called the Multitarget Tracking - Fuzzy Data Association “MTT-FDA” tracker, employs fuzzy variables capable of resolving the problem of multiple crossing targets. These variables are the rate of change of the target states over a sliding window. It has been observed through simulations that a window size of five time scans is sufficient to yield acceptable results. Moreover, the proposed tracker was exercised against the realistic multitarget data set. The results reveal that the proposed fuzzy tracker yields superior performance compared to other existing tracking schemes.
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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 | 0.000 | 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 | 0.001 | 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