Bibliographic record
Abstract
A decentralized Kalman filter strategy is presented and applied to GPS/INS (Global Positioning System/inertial navigation system) integration. Two Kalman filters are used. One is a local filter, processing GPS data and providing locally best estimates of position and velocity. The second is an INS filter which uses the results from the GPS filter as updates to the estimates obtained from the inertial data. Because of the high short-term accuracy of the inertial system, the position results from INS can be used for cycle slip detection and correction. The major advantages of this method are the flexible combination of GPS and INS and the simplicity of the implementation. Compared to centralized filtering, the decentralized filter gives globally the same optimal estimation accuracy as the centralized Kalman filter. The accuracy does not deteriorate when a suboptimal cascaded filter is used, which has some advantages in terms of computational efficiency. Extension of this method to more sensors is straightforward. Numerical results are used to illustrate the salient features of the method.>
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.003 | 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".