Multirate Sensor Fusion in the Presence of Irregular Measurements and Time-Varying Time Delays Using Synchronized, Neural, Extended Kalman Filters
Bibliographic record
Abstract
Sensor fusion plays a critical role in improving estimation accuracy of process quality variables. In this article, the dual, neural, extended Kalman filter (DNEKF) and the state model compensation neural, extended Kalman filter (SNEKF) are synthesized to compensate for modeling errors in the extended Kalman filter (EKF)-based multirate sensor fusion. Specifically, fusion is performed in the presence of irregularly sampled, slow-rate measurements with time-varying time delays. The proposed algorithm estimates the state and neural network parameters simultaneously through state vector augmentation. The estimated parameters of the state model compensation neural network (SNN) are shared between the DNEKF and SNEKF. It is demonstrated through two numerical examples that the proposed algorithm effectively reduces the estimation error under different conditions. In addition, it successfully improves the critical industrial quality variable estimation accuracy from the fast-rate soft sensor for over 20%, in terms of the mean squared error, demonstrating its advantages.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".