Reweighting Interacting Multiple-Model Algorithm to Overcome Model Competition for Target Tracking in the Hybrid System
Why this work is in the frame
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Bibliographic record
Abstract
The vicious competition of interacting multiple-model (IMM) algorithm is an inherent problem and would produce irreversible effects on IMM estimation results, especially combining with the radar system. In this article, a novel reweighting IMM (RIMM) is proposed to overcome this issue. First, the theoretical lower bound of model numbers in different situations is respectively provided through the analysis of IMM limitations. Furthermore, certificate the influence of model inaccuracy on the Kalman filter, which illustrates an effective method for reducing errors is increasing model numbers. Third, the definition of model set density and the analysis of the true model space are given, and their connection establishes the standard of how to design the model set or add the model number. Finally, an effective method called RIMM is provided to overcome the competition caused by model increasing. The proposed RIMM holds strong adaptability for different model sets. The simulations of RIMM highlight the correctness and effectiveness of the proposed methods.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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