Evaluation of the ionospheric corrections generated by smartphone and application to PPP-RTK
Bibliographic record
Abstract
The ionospheric delay is a major error source in the Global Navigation Satellite System (GNSS) positioning, and its accurate estimation is essential for Precise Point Positioning Real-Time Kinematic (PPP-RTK). Traditionally, ionospheric delay estimation relies on a network of permanently deployed high-end geodetic GNSS receivers, which are costly and thus inaccessible for most consumer applications. Moreover, this approach is limited by the sparse spatial distribution and low temporal resolution of the network, leading to significant estimation errors in uncovered environments.On the other hand, due to the global density and accessibility of low-cost GNSS receivers, such as smartphones, there is a strong demand to develop new methods for precise ionospheric delay estimation using them. Moreover, multi-frequency and multi-constellation GNSS chipsets are now embedded in smartphones including carrier phase observations essential for precise positioning. These advances support the investigation and development of new methods to enable precise real-time GNSS positioning even using smartphones. However, few studies have focused on the application and evaluation of such methods for PPP-RTK positioning.Therefore, this study aims to develop methods to estimate ionospheric effects using low-cost GNSS receivers and demonstrate that it can provide reliable ionospheric corrections. Additionally, we evaluated ionospheric corrections using two real-time satellite orbit, clock, and code bias products, namely the satellite-based BeiDou PPP-B2b and ground-based Centre National d’Etudes Spatiales (CNES). First, the ionospheric delay estimates generated by a single reference smartphone with uncombined PPP and quality control measures based on solution separation testing is evaluated using of the real-time satellite orbit, clock, and code bias products from BeiDou PPP-B2b and CNES, respectively. Next, the generated ionospheric delay from two correction models is compared to that produced by a high-end geodetic receiver. Finally, the generated ionospheric corrections are applied to single-station-based PPP-RTK to assess its positioning performance under kinematic conditions. A field test was conducted using two Google smartphones on April 7, 2024, in Calgary. We expect to achieve decimeter-level slant ionospheric corrections accuracy compared to geodetic receiver with the two correction models used. Additionally, the positioning accuracy is expected to approach that of PPP-RTK results using geodetic receivers as base stations, significantly outperforming float PPP.
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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".