Robust INS/GPS Coupled Navigation Based on Minimum Error Entropy Kalman Filtering
Why this work is in the frame
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Bibliographic record
Abstract
This paper addresses the results showing the expanded use or improvement of the accuracy, availability, and/or integrity performance of multisensory navigation systems. In addition, Processing algorithms and methods for multisensory systems are significantly improved when noises are non-Gaussian. In the literature, different modified linear and nonlinear Kalman filters (KFs) were derived under the Gaussian assumption and the well-known minimum mean square error (MMSE) criterion. In order to improve their robustness with respect to impulsive non-Gaussian noises, different algorithms and techniques based on Gaussian sum filtering, Huber based estimators and recently introduced maximum Correntropy criterion (MCC) have recently been used to counter the weakness of the MMSE criterion in developing different versions of robust Kalman filters.
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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