Consistent Multiple Model Horizon Scenario Tree (MM-HST) Framework for Optimization-Based State Estimators
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Bibliographic record
Abstract
State consistency in multiple model (MM) estimators is essential for applications that exhibit changes in their system dynamics, such as localization and target tracking. In this paper, we introduce a consistent multiple model horizon scenario tree (MMHST) framework for optimization-based estimators designed using sliding window (SW) optimization and inertial pre-integration. The problem is addressed by aligning the pre-integrated noise characteristics within the motion models to the system’s process noise, preserving the predefined assumptions under nominal conditions. Moreover, the combinatorial expansion of the MM-HST is analyzed, showing enhanced state consistency as the scenario tree weight increases. The developed design is tested in a varying motion dynamics environment, including constant velocity (CV) and constant turn (CT) models. Results show that the proposed MM-HST enhances accuracy and consistency compared to existing MM 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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