Sensor Fusion INS/GNSS based on Fuzzy Logic Weighted KalmanFilter
Bibliographic record
Abstract
A Fuzzy Logic Adaptive Control (FLAC) is used to adjust the exponential weighting parameter of a weighted Error-State Kalman Filter (ESKF) in an INS/GNSS system. The FLAC is used to prevent the Kalman Filter (KF) from diverging or to reach to a high bound when the IMU produces colored noise. Furthermore, a matrix notation for the weighting parameter alpha is introduced and compared against the single alpha value. First, the results show the influence of a colored noise in the system, which makes the ESKF reaching a large error bound solution. The application of FLAC considering both constant and matrix alpha reduces the error boundary for the position and velocity states. However, the constant alpha leads to an inaccurate altitude, bias correction, and error covariance matrix. The matrix alpha parameter shows a final solution that improves the navigation accuracy for all states, preserving the stability of the error covariance matrix.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".