Sensor Fusion INS/GNSS based on Fuzzy Logic Adaptive Error-State Kalman Filter and Unscented Kalman Filter
Bibliographic record
Abstract
In this work, a Fuzzy Logic Adaptive Control (FLAC) is used to correct an Error-State Kalman Filter (ESKF) and an Unscented Kalman Filter (UKF) in a loosely coupled INS/GNSS system.The FLAC is used to prevent the Kalman Filter (KF) to diverge or to reach to a high bound solution when the Inertial Measurement Unit (IMU) presents a dominant 1/f flicker noise.First, the ESKF and UKF implementation were tuned to achieve the optimal solution when the IMU has only white noise.Secondly, a 1/f flicker noise was applied to the IMU, making both Kalman Filters implementation achieve a suboptimal solution.And thirdly, a FLAC was used to correct both ESKF and UKF when coloured noise is present.The results evidence the influence of coloured noise in the system, which makes both Kalman Filter implementations reach to a large error bound solution.After analyzing the Kalman Filter behaviour with coloured noise, a novel FLAC methodology was defined.The FLAC combines the observation of both the residuals and the states error covariance and apply the correction using the exponential weighted parameter when the error covariance presents a higher than expected value, and a process noise injection when the residuals are broader than expected.The application of the proposed FLAC methodology figures out as the best solution to deal with the coloured noise, leading to a final solution that improves the navigation accuracy for all the states, preserving the stability of the error covariance matrix.Finally, the results for ESKF are compared against the results for the UKF.It was showed that, although both Kalman filter implementations bring equivalent outcomes, the UKF is slightly less sensitive to disturbances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".