Investigating Galileo Signal Tracking Challenges in Smartphones
Bibliographic record
Abstract
Since 2016, Android smartphones have allowed access to the raw GNSS data, leading to significant improvements in positioning accuracy. Modern devices now support dual-frequency GNSS and multiple satellite systems, making positioning more reliable. Despite significant efforts in GNSS smartphone positioning, including using the Galileo constellation, several important issues still need to be addressed. Galileo signals have more complex modulation schemes compared to GPS signals. Practical tests show that after a short period, Galileo measurements' status may change from 'TOW Decoded' to 'E1C 2nd Code' status, where TOW represents the GNSS time of week. The receiver can stay on the 'E1C 2nd Code' status for several minutes. Some Galileo-ready chips track the data component to decode the navigation message. Once the ephemerides and clock data are decoded, they switch to tracking the E1C (pilot component), resulting in the 'E1C 2nd Code' status and ambiguous pseudoranges. In the current Galileo tracking approach, some satellites remain in 'TOW Known' or 'TOW Decoded' status for over an hour, while others switch to 'E1C 2nd Code Lock', resulting in ambiguous pseudoranges. The algorithm used to determine the tracking status for each satellite remains unclear. The white paper published by the European GNSS Agency’s (GSA) recommends checking the Galileo tracking status and highly advises using Galileo measurements only when in the E1C 2nd Code status.In this research, we will show that a 4 ms jump is still observed in some datasets, even though the tracking status is E1C 2nd Code. This confirms that verifying the signal tracking status alone is insufficient, as 4 ms jumps in the data can still occur despite this check. During these "jump epochs," erroneous measurements can adversely affect positioning accuracy. To investigate this issue, data collected by the Xiaomi Mi8 and Google Pixel 8 Pro devices are used. The results indicate that these jumps vary between devices and over time. The results also show that these jumps still occur, even though the tracking status is E1C 2nd Code.This 4 ms jump has also been addressed by Galluzzo et al. (2018) during the 2018 IPIN conference. They proposed a straightforward method to correct the pseudorange by detecting jumps through the difference between two consecutive epochs. If the difference is around 4 ms, the subsequent pseudoranges are adjusted accordingly. Although the theory behind this method is straightforward and effective in many cases, it cannot detect all jumps, for example, when those satellites first appear or when the pseudorange is missing. In this research, we employ the Observation Minus Calcaulation (OMC) to solve this issue and find the undetected 4 ms jumps. Finally, we investigate the accuracy of the kinematic data from Xiaomi Mi8 and Google Pixel 8 Pro devices with and without corrections for the 4 ms jumps. The results showed performance improvement in terms of the root mean square (RMS) and the 50th percentile of the horizontal positioning error after applying the correction for the 4 ms jump in Galileo measurements.
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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 | 0.001 |
| 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".