An Impact of High Ionospheric Activity on MultiRef RTK Network Performance in Japan
Bibliographic record
Abstract
A Network RTK system using MultiRefTM software, was introduced in Japan by the GPS Division of DX Antenna (now GNSS Technologies Inc.), Hitachi Industrial Equipment Systems Co., Ltd, Roberton Enterprises Ltd. (now Forgis Technologies Inc.) and TV Asahi with strong support of the University of Calgary (see our ION GPS- 2000/2001 papers). The infrastructure for this system was created and a tested during September 2000 in Tokyo. The introduction attracted more than 300 representatives from different companies. The test conducted at that time demonstrated the ability of the system to provide a 2-5 cm level accuracy over the baselines longer than 30 km. The developed infrastructure implements Internet-based reference stations and two types of data link between a Control Center, which operates MultiRefTM software, and user. The first type of data link is a unidirectional data channel which uses TV audio sub-carrier. The second data link is bi-directional through a cellular phone. The proposed service must provide the user with RTK corrections and help extend the baseline distance between the user and the nearest reference station. The RTK baseline is usually limited to a distance of less than 10 km because the differential errors between reference station and rover could exceed the GPS signal wavelength and will therefore interfere with the carrier phase ambiguity resolution algorithms implemented at the rover. Fortunately, most of these errors de-correlate linearly with distance. These errors are atmospheric and orbital. Among these errors, the most significant are the ionospheric errors. Using errors at known locations of the reference stations, one can find the approximate magnitude of the error between at user position. In this case the user receives corrections that are calculated based on all network data. These corrections can be used to simulate corrections for a nonexistent, virtual reference station, which is located near the user position. Usual offthe- shelf RTK receiver can use this data. Alternatively the corrections can be transmitted to the user in a form of a correction grid but this would require special software to decode the data. Although the exciting results were showed during the demonstration, it appears that system has insufficient for commercial service reliability of RTK Network performance due to the ionospheric activity. The main challenge for this approach lies in the fact that the Control Center software has to resolve ambiguities over even longer baseline between network reference stations in order to calculated spatial corrections. Factors, which can assist this process, are as follows: 1. The position of the network reference stations are known with high accuracy. 2. The reference stations are static and some extra time averaging can be used for this estimation. The downside is the satellite cannot be used until it reaches certain elevation. We have demonstrated that even without any obstructions around the reference station location, the number of satellites in view above this elevation can be rather limited and not enough for successful RTK. This issue introduces some difficulties in providing a continuously reliable solution. However, the main problem is reliable and quick ambiguity resolution over the network in real time. In Japan these difficulties were caused by severe ionospheric conditions, which in turn had a negative impact on the ambiguity resolution over the network. This caused by the two reasons, one is that we are currently on a top of 11-year solar activity cycle. The other is that Japan is one of less favorable regions in terms of ionospheric activity due to its geographical location. In order to cope with the increased ionospheric activity, the algorithms need to be modified to enhance ambiguity resolution over the network. Ionospheric activity affects MultiRefTM performances in the following ways: 1. It affects ambiguity resolution when overall error exceeds approximately half of the wavelength. 2. It interferes with correction calculation algorithm, because of fluctuations between the grid points. The paper presents an estimate of these factors. 3. High ionospheric activity causes phase scintillation in GPS signal and cycle slips as a result. To address the issue of corrections calculation the we analyzed different methods for corrections interpolation. An optimization of network configuration can improve accuracy without increasing the number of reference stations. We examined a way to decrease ionospheric error through real time ionosphere mapping as a function of station coordinates, and satellite elevation. This mapping function has an advantage that, although it less accurate than satellite-to-station and epoch-by-epoch estimate, it doesn’t need as much time for employment. The ionospheric mapping function is proposed to be estimated at a slow rate to assist ambiguity resolution, which in turn is used for ionosphere estimation. The algorithm does however require time for initialization in case of real-time implementation. This algorithm is coupled with a special calibration technique. In conjunction with this estimation of tropospheric errors, antenna phase center displacement, multipath and orbital errors using different spans of data allows ionospheric errors to be more observable. The proposed calibration technique allows estimation of the repetitive errors including antenna phase center displacement, multipath, and most of the ionospheric errors. This calibration technique also allows improvement of real time estimates of the ionospheric errors by using model enhancement. The proposed techniques allow to extend RTK method to a long baseline and improve its accuracy. The derivative of this technique, which uses spatio-temporal correlations of double difference residuals and their variations can be used in different applications, where real-time centimeter level accuracy over long baseline is required. Test and computer simulation results are presented.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".