Bibliographic record
Abstract
In this dissertation, a multi-sensor integrated system is developed to provide an accurate positioning solution under open-sky, challenging environments such as downtown areas and GNSS-denied environments such as indoor parking lots. A PPP system is first developed by utilizing a geodetic-grade GNSS receiver. An Improved Robust adaptive Kalman Filter (IRKF) is adopted and used as the estimation filter to compensate for the GNSS measurement outliers and the dynamic error modeling. Centimeter-level horizontal positioning accuracy is achieved under an open sky environment, while decimeter-level horizontal positioning accuracy is achieved under a challenging environment. An IRKF-based PPP/INS integration algorithm is then developed by utilizing a geodetic-grade GNSS receiver and a tactical-grade IMU. The integrated system is assessed through two ground vehicular field trials. The developed integrated system achieves centimeter-level positioning accuracy under open-sky environments and decimeter-level positioning accuracy under simulated GNSS outages. Furthermore, the IRKF-based integrated system achieves attitude accuracy of 0.052º, 0.048º, and 0.165º for pitch, roll, and azimuth angles, respectively. Thereafter, the performance of the dual-frequency (DF) Xiaomi mi 8 smartphone is tested in static and kinematic PPP modes. The smartphone-based PPP solution achieves decimeter-level positioning accuracy in the static mode and meter-level positioning accuracy in kinematic mode. A DF u-blox GNSS receiver and xsens industrial-grade MEMS IMU are further used to develop an ultra-low-cost PPP/INS integrated system. The integrated system achieves sub-meter-level positioning accuracy in both the north and up directions, and meter-level positioning accuracy in the east direction. Additionally, the integrated GNSS PPP/INS system achieves attitude accuracy of about 0.878°, 0.804°, and 2.905° for the pitch, roll, and azimuth angles, respectively. To provide an accurate positioning solution for GNSS-denied environments, a LiDAR odometry (LO)/INS/simulated ultra-wide band (UWB) integrated system is developed. The simulated UWB solution is used as an external frequent update to augment the accuracy of the LO/INS solution. Meter-level horizontal positioning accuracy is achieved through the LO/INS integration with frequent simulated UWB-based updates.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".