Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction
Why this work is in the frame
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Bibliographic record
Abstract
To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as "filter fault"), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
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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 | 0.000 | 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 | 0.000 | 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