A fuzzy-augmented Kalman filter for IMU/GPS integration
Why this work is in the frame
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Bibliographic record
Abstract
Most of the present techniques for integrating Inertial Measurement Units (IMU) and Global Positioning Systems (GPS) utilize Kalman filtering (KF) as the integration estimation technique. KF is a recursive algorithm designed to compute corrections to a system based on external measurements. In inertial navigation, this can be accomplished by using an external navigation reference such as GPS. As long as GPS measurements are available, the KF solution of IMU/GPS integration works efficiently and provides accurate estimate of the navigation states. Nevertheless, during GPS signal outages, the functionality of KF update engine is disrupted due to the lack of GPS update measurements and therefore KF works only in prediction mode. Moreover, IMUs, particularly those integrating low-cost sensors, suffer from one serious limitation: drift rate errors rapidly accumulate with the passage of time. As a result, the corresponding state estimate will also quickly drift over time causing a dramatic degradation in the overall accuracy of the integrated system. Performance improvements of integrated IMUs, utilizing low-cost sensors, and GPS are presented in this paper. This achieved through the implantation of a new technique which augment KF and Fuzzy logic principles. In the innovation in the new technique is in its ability to generate the update measurements (positions and velocity error measurements) to the KF update engine even during GPS signal outages. This proposed technique has been tested on real MEMS inertial and GPS data collected in a land vehicle navigation test. The test results indicate that the proposed Fuzzy model can efficiently compensate for GPS updates during short GPS signal outages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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