Experimental results of an adaptive fuzzy network Kalman filtering integration for low cost navigation applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The performance of Kalman filter is highly dependent on the availability of two basic requirements: (1) accurate dynamic modeling and (2) proper measurements that fit this model. The absence of either of those two requirements will degrade the Kalman performance over time particularly during the absence of the reference signal frequently used to update the estimated Kalman states. To overcome such problem, a new design model, namely fuzzy-Kalman, integrating fuzzy logic systems and adaptive Kalman filtering for the integration of IMU and GPS is developed in this paper. The developed model was tested using an integrated GPS/IMU for land-vehicle navigation applications. The results indicated that, unlike traditional Kalman, the proposed fuzzy-Kalman model could efficiently bridge short-time outages of reference signal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.002 |
| 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