Real-Time UWB and IMU Fusion Positioning System for Urban Rail Transit with High Mobility
Bibliographic record
Abstract
Urban rail transit is currently moving toward automated driving and mobile occlusion, which raises higher expectations for the train positioning system. Operating in diverse environments like open spaces and tunnels, a single positioning system falls short in meeting the accuracy and consistency requirements for train positioning along the entire rail line. In response to these stringent requirements, this research proposes an Error-State Kalman Filter (ESKF) based real-time fusion train positioning system for urban rail transit, incorporating Ultra-Wideband (UWB) and Inertial Measurement Unit (IMU) technologies. Through practical measurements, an UWB ranging error model is established. Based on this model, a simulation study is conducted on the proposed real-time fusion positioning algorithm under different UWB anchor deployment methods in the context of urban rail transit scenarios. The real-time performance and accuracy of the proposed algorithm were validated through in-tunnel testing.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".