Integration of precise point positioning and reduced inertial sensors system
Bibliographic record
Abstract
In Global Positioning System (GPS), Precise Point Positioning (PPP) achieves the highest accuracy in point positioning. It approaches centimetre-level accuracy in static mode and sub-decimetre accuracy in kinematic mode. PPP is an alternative approach to carrier-phase-based Differential GPS (DGPS) and offers advantages over DGPS. PPP uses GPS observations from a single receiver for position estimation, which is simpler than using more than one GPS receiver. However, PPP needs rigorous modelling for all errors and biases, which are otherwise cancelled out or mitigated when using DGPS. PPP’s popularity is on the rise, as it is ideal for land-vehicle positioning and navigation. However, in challenging environments, PPP suffers from a signal loss that prevent continuous navigation or a reduction in the number of visible satellites that causes accuracy degradation. This research integrates PPP with a Reduced Inertial Sensors System (RISS) — a low-cost system that uses data from reduced MEMS-based inertial sensors and vehicle odometry — to provide accurate and inexpensive land-vehicle navigation systems. The system is integrated in a tightly coupled mode through the use of an Extended Kalman Filter (EKF), which employs an improved error model for the RISS data. The system was tested using data from real driving routes with single-frequency code-based PPP/RISS (SF-code-PPP/RISS), dual-frequency code-based PPP (DF-code-PPP/RISS), smoothed dual-frequency code-based PPP (S-DF-code-PPP/RISS), and code- and carrier-phase-based PPP (code-carrier-PPP/RISS). The performance of the developed PPP/RISS was evaluated using position RMS and maximum errors during continuous GPS availability as well as during signal outages. The developed integrated algorithms were assessed using three real road tests that capture different navigational conditions. The results show that when five or more satellites are available, code-carrier-PPP/RISS solution is superior to that of SF- and DF-code-PP/RISS. For latitude, code-carrier-PPP/RISS solution was 47% and 20% more precise than the SF- and DF-code- PP/RISS counterparts, respectively. For longitude, code-carrier-PPP/RISS solution was 65% and 31% more precise than the SF- and DF-Code-PP/RISS counterparts, respectively. Similarly, the altitude solution was improved by 46% and 25%, respectively. During GPS signal outages of 60 seconds, code-carrier-PPP/RISS’s algorithms outperformed that of SF- and DF-code-PPP/RISS by about 35% when the satellite availability level was set to three satellites. For other satellite availability levels, the algorithms performed almost identically.
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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 | 0.000 | 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 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".