Assessment of Multi-constellation RTK Solutions During Differential Correction Data Outages
Bibliographic record
Abstract
Global Navigation Satellite System (GNSS) receivers that provide a Real Time Kinematic (RTK) solution using carrier phase techniques generally perform differential carrier phase positioning utilizing the pseudorange and phase correction data from a reference (or base) receiver. The rover receivers obtain these corrections via radio link or data over Internet Protocol (IP) and utilize them to provide precise position solutions using carrier phase measurements. The use of correction data removes spatially correlated errors such as satellite orbital errors, atmospheric delays and clock effects. In order to achieve centimetre-level position accuracy, the rover should solve the double differenced integer ambiguities. However, the accuracy of the obtained solution depends on the distance between reference station and rover, termed as the baseline. Nominally short baselines of the order of 1-10 km result in centimetre-level accuracy and with longer baselines (> 40-80 km, depending on the level of ionospheric activity) the accuracy degrades to decimetre or metre-level which lead to potential incorrect integer ambiguities resolution, especially on the shortest wavelength carrier phase measurements. In order to continuously provide an accurate solution the rover must receive uninterrupted correction data from the reference station. Unfortunately there is a possibility that the correction data from reference station is unavailable to the rover for various intervals. The reasons for non-availability of correction data could be radio frequency interference or network outages. This momentary or long-term communication breakdown impacts the accuracy of the obtained solution. The goal of this research is to provide an assessment of accuracy and reliability of multi-constellation RTK solution during differential correction data outages. The proposed work begins with analysis of GPS, GPS/GLONASS, and GPS/GLONASS/Galileo multi-constellation RTK solutions. Much work has been done in GPS/GLONASS multi-constellation based solution using live data. However, the majority of the work involving the Galileo constellation is limited to simulations due to unavailability of live data. The proposed work involves utilizing live data from GPS, GLONASS and two to four Galileo satellites, subject to their availability in mid 2013 to obtain a triple constellation RTK solution that includes a tight integration of GPS and Galileo on common carrier frequencies. The performance of the multi-constellation solution will be compared to GPS alone, and this analysis will then be expanded to include various correction data outage periods ranging from 0 to tens of minutes. The effect of the correction data outage period on solution accuracy also depends on whether the rover is stationary or in motion; both cases will be analyzed. The proposed work consists of multi-constellation GNSS data collection with various baselines ranging from one to 40 km for both static and vehicular dynamics rover scenarios in open sky environments. The correction data outages ranging from 0 to tens of minutes will be introduced to assess the performance of the solution. The proposed work will use an RTK software platform developed by the PLAN group at University of Calgary, namely PLANSoftTM. The outcome will be a comparative study of the solution accuracy at mentioned correction data outage periods for defined baselines. The proposed work will elaborate on the understanding of the impact of correction data outages on position accuracy by analysing various combinations of multi-constellation solutions such as GPS only, GPS/GLONASS, GPS/Galileo, and GPS/GLONASS/Galileo. The use of satellite measurements from multi-constellation systems against a single-constellation system increases observability during correction data outages. Therefore it is anticipated that the solution accuracy from GPS/GLONASS/Galileo multi-constellation system will outperform other combinations considered in this work. Further, various combinations of multi-frequency measurements will be formed and the results will be discussed. These combinations include GPS L1 and L2, GLONASS G1 and G2, and Galileo E1, E5a, and E5b signals. In addition to the analysis of accuracy degradation during correction data outages, a study of accuracy improvement when the correction data is re-introduced after a specific test period is also done. Particularly, the amount of the time taken by the navigation solution to converge to nominal accuracy level will be assessed. The complete assessment will provide a broad understanding on accuracy of RTK solutions during differential correction data outages. In addition, this analysis is useful to determine the minimum data rate to transfer correction data, which can meet specific accuracy requirements.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.002 | 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".