<title>Tracking highly maneuverable targets in clutter using interacting multiple-model fuzzy-logic-based tracker</title>
Bibliographic record
Abstract
The Interacting Multiple Model (IMM) estimator is a suboptimal hybrid filter that has been shown to be one of the most cost-effective hybrid state estimation schemes. The algorithm has the ability to estimate the state of a dynamic system with several modes which can switch from one mode to another. It is also considered to be the best compromise between the complexity and the performance. It is mainly used for tracking highly maneuvering targets in the presence of clutter by invoking the Probabilistic Data Association (PDA) in the estimator structure, also called IMM-PDA. Recently, it has been shown that the PDA technique does not perform well when tracking targets at low signal to noise ratios (SNR). An alternative technique to data association is the Fuzzy Data Association (FDA) which has the ability to track targets in clutter and in a low SNR environment. In this paper, an IMM-FDA technique is proposed for tracking highly maneuvering targets in clutter and in a low SNR environment. Simulations have been conducted to compare the performance of the proposed approach with that of the IMM-PDA. A typical scenario for a highly maneuvering target is considered as a tracking example. The simulation results reveal that both the trackers perform well when tracking the maneuvering target at high SNR. At low SNR, only the IMM-FDA is able to track the target accurately.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".