Heading accuracy improvement of MEMS IMU/DGPS integrated navigation system for land vehicle
Bibliographic record
Abstract
Many researches indicated that in land vehicle-based MEMS IMU/DGPS integrated navigation system, the vehicle heading is unobservable and its error can grow significantly fast with time, if the vehicle moves with only slow changes in attitude and acceleration, e.g. the vehicle moving along a straight road at almost constant velocity. In this paper, a new heading measurement is derived from the DGPS positions and this new measurement can improve the heading accuracy of MEMS IMU/DGPS integrated navigation system for land vehicle. However, the DGPS-derived heading will have a significant deviation from the true heading value while the vehicle makes a turn. Thus a sequential Kalman filter is proposed to process the DGPS position and heading measurements in a sequential order with MEM IMU measurements. This ensures the DGPS position measurements still can be used in the KF even if the DGPS heading measurements are unusable due to large deviation to the truth. To ensure the quality of the DGPS heading measurements, an innovation detection method is used to detect and reject the singular DGPS heading measurement from the sequential Kalman filter. A field test was conducted to test the effect of this new heading measurement on improving land vehicle heading accuracy. The test results showed that this new type of measurement can significantly reduce the heading error of MEMS IMU/DGPS integrated navigation solution from about 5 deg to less than 1 deg. Test results also showed that the innovation detection method can effectively control the quality of DGPS heading measurement. Without this control, the singular heading measurement would lead to a heading error as large as 100 deg. In summary, the introduction of DGPS-derived new heading measurement and the innovation detection method investigated in this paper can significantly improve the accuracy and reliability of the heading parameter in land vehicle MEMS IMU/DGPS integrated navigation system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".