A new weight updating method for INS/GPS integration architectures based on neural networks
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Bibliographic record
Abstract
Inertial navigation system (INS) and global position system (GPS) technologies have been widely utilized in many positioning and navigation applications. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers a number of advantages and overcomes each system's inadequacies. INS/GPS integration is usually implemented using Kalman filters. However, Kalman filters perform adequately only under certain predefined dynamic models and suffer from several problems related to observability and immunity to noise effects. An INS/GPS integration method based on artificial neural networks (ANNs) to fuse INS measurements and differential global positioning system (DGPS) measurements has been recently suggested. Although able to provide high performance INS/DGPS integration with accurate prediction of position components during GPS outages, the conventional methods of updating the ANN weights limit the real-time capabilities. This paper offers a new weight updating criterion to improve the limitation of traditional weight updating methods with the utilization of two different architectures; the position update architecture and position and velocity update architecture.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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