FIGURE 2. TEC map over São Paulo state as forecast by the CALIBRA model on Sept. 26, 2012, at 2:00 UT. The range of the TEC in the image is from 0 to 90 TEC units (blue to red). The red line is the geomagnetic equator.
Countering Ionospheric Disturbances Affecting GNSS in Brazil
By Marcio Aquino
After 27 months of intense research, the CALIBRA project ended successfully in February 2015, with the project team devising solutions to tackle the effects of perturbations typical of the Brazilian ionosphere on high-accuracy GNSS positioning. CALIBRA was funded by the European Union and the European GNSS Agency.
Kicked off in 2012, CALIBRA first confirmed the vulnerability of GNSS high-accuracy techniques to ionospheric disturbances through a thorough user performance review, where degradation in GNSS Precise Point Positioning (PPP) and real-time kinematic (RTK) positioning was seen to correlate with the occurrence of ionospheric scintillation and high Total Electron Content (TEC) variability. This is especially so in Brazil, because of its geographical location extending across the magnetic equator in one of the most troublesome ionospheric regions of the Earth, qualifying the country as a test-bed for worst-case scenarios.
The team established a suitable metric to characterize these disturbances, which was used in developing the new models and algorithms to counter their effects. The short-term empirical CALIBRA Forecasting Model (CFM) for TEC and scintillation was developed and tested.
To counter scintillation, a number of approaches were proposed and their benefits demonstrated. Building on the project’s success, CALIBRA partner INGV (Istituto Nazionale di Geofisica e Vulcanologia) filed a patent for the CFM and a new spin-off company — SpacEarth Technology — was set up. SpacEarth aims to secure the software’s commercialization for potential applications and services, while also improving and adapting it to evolving market needs.
Another outcome of commercial interest is that project partner Septentrio developed several rover-level mitigation approaches, notably a new model for ionospheric delay estimation.
Monitoring Network. To support the research and operational activities of the project, a dedicated network of ionospheric scintillation monitor receivers (ISMRs) was deployed, forming the CIGALA-CALIBRA network of 12 monitoring stations equipped with PolaRxS receivers. A web interface for data analysis — the ISMR Query Tool — was developed by project partner UNESP (São Paulo State University) and is available for public use, collecting and treating more than 10 million observations of GPS, GLONASS, Galileo, BeiDou and other augmentation systems on a daily basis. Data visualization and data mining techniques support users in data analysis and knowledge extraction.
Finally, two important field trials aiming to validate the new algorithms were carried out in Brazil, involving actual precision agriculture and offshore operations. For the precision agriculture trial, the Brazilian company Agro Pastoril Campanelli provided expert operational environment and support.
The tractor used in the precision agriculture trial at Agro Pastoril Campanelli’s premises.
For the offshore trial, the project counted on the collaboration of the DOF Brasil Group representing Norskan Offshore, a provider of high-end offshore services to the Brazilian oil and gas industry. Detailed results of both trials are in the project’s final report, which can be accessed through the GSA.
The Geograph vessel is operated by DOF Brasil.Setting up the receiver antenna for the offshore trial on board the Geograph vessel.
To provide a glimpse of the performance of the CALIBRA algorithms during the offshore trial, in FIGURE 1 we selected a period when strong scintillation conditions were encountered. In the top plot, two height component time series for kinematic PPP processing are shown, respectively, for the case where no mitigation is applied (black time series) and the case where the CALIBRA algorithm is applied (red time series).
FIGURE 1. Performance of CALIBRA algorithms in the offshore trial.
The bottom plot shows the level of amplitude scintillation (S4 index) affecting the GPS satellites over a 10-degree elevation angle.
The improvement obtained with the CALIBRA solution can be seen in particular during the PPP convergence period (18:00 to 18:30 UT) and during the period of strong scintillation (22:30 to 23:30 UT). As there was no accurate ground truth available, the RMS values with respect to the mean height, taken from the quiet period (between 19:00 and 22:00 UTC), along with the percentage of improvement when applying the CALIBRA mitigation approach are summarized in TABLE 1.
TABLE 1. RMS values with respect to mean height, 19:00–22:00 UTC.
Despite all the successful work carried out by CALIBRA, the team notes that research must be continued to accomplish further improvement in models and algorithms to finally develop processes for real-time operation. The challenge would be to counter these ionospheric threats in the scope of an operational service aimed to provide robust high-accuracy positioning to support user applications.
Furthermore, there were strong indications that the addition of Galileo will assist in mitigating the problems addressed in the project when more signals are available in space.
Marcio Aquino is a Principal Research Fellow at the Nottingham Geospatial Institute of Nottingham University and leader of CALIBRA.
Handling the Biases for Improved Triple-Frequency PPP Convergence
By Denis Laurichesse
Precise point positioning (PPP) can be considered a viable tool in the kitbag of GPS positioning techniques. One precision aspect of PPP is its use of carrier-phase measurements rather than just pseudoranges. But there is a catch. Often many epochs of measurements are needed for a position solution to converge to a sufficiently high accuracy. In this month’s column, we look at how using measurements from three satellite frequencies rather than just two can help.
INNOVATION INSIGHTS by Richard Langley
PPP? WHAT’S THAT? This acronym stands for precise point positioning and, although the technique is still in development, it has evolved to a stage where it can be considered another viable tool in the kitbag of GPS positioning techniques. It is now supported by a number of receiver manufacturers and several free online PPP processing services. You might think, looking at the name, that there’s nothing particularly special about it. After all, doesn’t any kind of positioning with GPS give you a precise point position including that from a handheld receiver or a satnav device? They key word here is precise.
The use of the word precise, in the context of GPS positioning, usually means getting positional information with precision and accuracy better than that afforded by the use of L1 C/A-code pseudorange measurements and the data provided in the broadcast navigation messages from the satellites. A typically small improvement in precision and accuracy can be had by using pseudoranges determined from the L2 frequency in addition to L1. This permits the real-time correction for the perturbing effect of the ionosphere. Such an improvement in positioning is embodied in the distinction between the two official GPS levels of service: the Standard Positioning Service provided through the L1 C/A-code and the Precise Positioning Service provided for “authorized” users, which requires the use of the encrypted P-code on both the L1 and L2 frequencies. Civil GPS users will have access to a similar level of service once a sufficient number of satellites transmitting the L2 Civil (L2C) code are in orbit. However, this capability will only provide meter-level accuracy. The PPP technique can do much better than this.
It can do so thanks to two additional precision aspects of the technique. The first is the use of more precise (and, again, accurate) descriptions of the orbits of the satellites and the behavior of their atomic clocks than those included in the navigation messages. Such data is provided, for example, by the International GNSS Service (IGS) through its global tracking network and analysis centers. These so-called precise products are typically used to process receiver data after collection in a post-processing mode, although real-time correction streams are now being provided by the IGS and some commercial entities.
Now, it’s true that a user can get high precision and accuracy in GPS positioning using the differential technique where data from one or more base or reference stations is combined with data from the user receiver. However, by using precise products and a very thorough model of the GPS observables, the PPP technique does away with the requirement for a directly accessed base station.
The other precision aspect of PPP is its use of carrier-phase measurements rather than just pseudoranges. Carrier-phase measurements have a precision on the order of two magnitudes (a factor of 100) better than that of pseudoranges. But there is a catch to the use of carrier-phase measurements: they are ambiguous by an integer multiple of one cycle. Processing algorithms must resolve the value of this ambiguity and ideally fix it at its correct integer value. Unfortunately, it is difficult to do this instantaneously, and often many epochs of measurements are needed for a position solution to converge to a sufficiently high accuracy, say better than 10 centimeters. Researchers are actively working on reducing the convergence time, and in this month’s column, we look at how using measurements from three satellite frequencies rather than just two can help.
“Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. To contact him, see the “Contributing Editors” section on page 6.
While carrier-phase measurements typically have very low noise compared to pseudorange (code) measurements, they have an inherent integer cycle ambiguity: the carrier phase, interpreted as a range measurement, is ambiguous by any number of cycles. However, integer ambiguity fixing is now routinely applied to undifferenced GPS carrier-phase measurements to achieve precise positioning. Some implementations are even available in real time. This so-called precise point positioning (PPP) technique permits ambiguity resolution at the centimeter level.
With the new modernized satellites’ capabilities, performing PPP with triple-frequency measurements will be possible and, therefore, the current dual-frequency formulation will not be applicable. There is also a need for a generalized formulation of phase biases for Radio Technical Commission for Maritime Services (RTCM) State Space Representation (SSR) needs. In this RTCM framework, the definition of a standard is important to allow interoperability between the two components of a positioning system: the network side and the user side.
Classical Formulation
In this section, we review the formulation of the observation equations. We will use the following constants in the equations:
where f1 and f2 are the two primary frequencies transmitted by all GPS satellites and c is the vacuum speed of light. For the GPS L1 and L2 bands, f1 = 154f0 and f2 = 120f0, where f0 = 10.23 MHz.
The pseudorange (or code) measurements, P1 and P2, are expressed in meters, while phase measurements, L1 and L2, are expressed in cycles. In the following, we use the word “clock” to mean a time offset between a receiver or satellite clock and GPS System Time as determined from either code or phase measurements on different frequencies or some combination of them.
The code and phase measurements are modeled as:
(1)
where:
D1 and D2 are the geometrical propagation distances between the emitter and receiver antenna phase centers at f1 and f2 including troposphere elongation, relativistic effects and so on.
W is the contribution of the wind-up effect (in cycles).
e is the code ionosphere elongation in meters at f1. This elongation varies with the inverse of the square of the carrier frequency and is applied with the opposite sign for phase.
Δh = hi – hj is the difference between receiver i and emitter j ionosphere-free phase clocks. Δhp is the corresponding term for code clocks.
Δτ = τi – τj is the difference between receiver i and emitter j offsets between the phase clocks at f1 and the ionosphere-free phase clocks. By construction, the corresponding quantity at f2 is γΔτ. Similarly, the corresponding quantity for the code is Δτp (time group delay).
N1 and N2 are the two carrier-phase ambiguities. By definition, these ambiguities are integers. Unambiguous phase measurements are therefore L1 + N1 and L2 + N2.
Equations (1) take into account all the biases related to delays and clock offsets. The four independent parameters, Δh, Δτ, Δhp, and Δτp, are equivalent to the definition of one clock per observable. However, our choice of parameters emphasizes the specific nature of the problem by identifying reference clocks for code and phase (Δhp and Δh) and the corresponding hardware offsets (Δτp and Δτ). These offsets are assumed to vary slowly with time, with limited amplitudes.
The measured widelane ambiguity, , (also called the Melbourne-Wübbena widelane) can be written as:
(2)
whereNwis the integer widelane ambiguity,μ jis the constant widelane delay for satellite j and μiis the widelane delay for receiver i (which is fairly stable for good quality geodetic receivers). The symbolmeans that all quantities have been averaged over a satellite pass.
Integer widelane ambiguities are then easily identified from averaged measured widelanes corrected for satellite widelane delays. Once integer widelane ambiguities are known, the ionosphere-free phase combination can be expressed as
(3)
whereis the ionosphere-free phase combination computed using the known Nw ambiguity,Dcis the propagation distance,hiis the receiver clock and h jis the satellite clock. N1is the remaining ambiguity associated to the ionosphere-free wavelengthλc(10.7 centimeters).
The complete problem is thus transformed into a single-frequency problem with wavelengthλcand without any ionosphere contribution. Many algorithms can be used to solve Equation (3) using data from a network of stations. IfDcis known with sufficient accuracy (typically a few centimeters, which can be achieved using a good floating-point or real-valued ambiguity solution), it is possible to simultaneously solve for N1 ,hiand h j. The properties of such a solution have been studied in detail. A very interesting property of theh jsatellite clocks is, in particular, the capability to directly fix (to the correct integer value) the N1values of a receiver that was not part of the initial network.
The majority of the precise-point-positioning ambiguity-resolution (PPP-AR) implementations are based on the identification and use of the two quantitiesμ jand h j. These quantities may be called widelane biases and integer phase clocks, a decoupled clock model or uncalibrated phase delays, but they are all of the same nature.
A Real-Time PPP-AR Implementation
A PPP-AR technique was successfully implemented by the Centre National d’Etudes Spatiales (CNES) in real time in the so-called PPP-Wizard demonstrator in 2010 and has been subsequently improved. In this demonstrator and in the framework of the International GNSS Service (IGS) Real-Time Service (RTS) and the RTCM, the GPS and GLONASS constellation orbits and clocks are computed. Additional biases for GPS ambiguity resolution are computed and broadcast to the user. The demonstrator also provides an open-source implementation of the method on the user side, for test purposes. Centimeter-level positioning accuracy in real time is obtained on a routine basis.
Limitations of the Bias Formulations. The current formulation works but it has several drawbacks:
The chosen representation is dependent on the implemented method. Even if the nature of the biases is the same, their representation may be different according to the underlying methods, and this makes it difficult for a standardization of the bias messages.
The user side must implement the same method as the one used on the network side. Otherwise, the user side would have to convert the quantities from one method to another, leading to potential bugs or misinterpretations.
It is limited to the dual-frequency case. There are only two quantities to be computed in the dual-frequency case ( and ), but in the triple-frequency case, there are many more possible combinations. For example, one can have (this is a non-exhaustive list) , , ,, , , where the indices refer to different pairs of frequencies, and other ionosphere-free combinations such as phase widelane-only or even phase ionosphere-free and geometry-free combinations are possible.
New RTCM SSR Model
The new model, as proposed by the RTCM Special Committee 104 SSR working group for phase bias messages is based on the idea that the phase bias is inherent to each frequency. Thus, instead of making specific combinations, one phase bias per phase observable is identified and broadcast.
It is noted that this convention was adopted a long time ago for code biases. Indeed, in the RTCM framework, and unlike the standard differential code bias (DCB) convention where code biases are undifferenced but combined, the RTCM SSR code biases are defined as undifferenced and uncombined. The general model for uncombined code and phase biases is therefore:
(4)
Time group delays, τ, and phase clocks, h, in Equation (1) are replaced by code and phase biases (ΔbP andΔbLrespectively). RTCM SSR code and phase biases correspond to the satellite part of these biases. The prime notation denotes the “unbiasing” process of the measurements. Here, the clock definition is crucial. As the biases are uncombined, they are referenced to the clocks. The convention chosen for the standard is natural: it is the same as the one used by IGS, that is,ΔhPin our notation.
This new model can be extended to the triple-frequency case very easily, as it does not involve explicit dual-frequency combinations:
(5)
This new model simplifies the concept of phase biases for ambiguity resolution. This representation is very attractive because no assumption is made on the method used to identify phase biases on the network side. All the implementations are valid if they respect this proposed model. It also allows convenient interoperability if the network and user sides implement different ambiguity resolution methods.
TABLE 1 summarizes the different messages used for PPP-AR in the context of RTCM SSR:
TABLE 1. RTCM SSR messages for PPP-AR.
Bias Estimation in the Dual-Frequency Case. The new phase biases identification in the dual-frequency case is straightforward. There are two biases (, ) to be estimated using two combinations (µ and h). The problem to be solved is described in FIGURE 1.
FIGURE 1. Phase biases estimation in the dual-frequency case.
It can be solved very easily on the network side by means of a 2 × 2 matrix inversion:
(6)
with
Note: All the quantities denote the satellite part of the Δ operator defined above.
Bias Estimation in the Triple-Frequency Case. The triple-frequency bias identification is tricky due to the need, using only three biases, to keep the integer nature of phase ambiguities on all viable ionosphere-free combinations, and in particular combinations that were not used in the identification process. At this level, one cannot make assumptions on what kind of combinations will be employed by a user. The problem to be solved is described in FIGURE 2.
FIGURE 2. Phase biases estimation in the triple-frequency case.
As an example, a naïve solution would be to identify the extra-widelane phase biases,, using the dual-frequency widelane approach, and then identify thebias. Given the large wavelength of the extra-widelane combination, such identification would be very easy. However, the corresponding bias would be only helpful for extra-widelane ambiguity identification, and its noise would prevent its use for widelane 15 (L1/L5) ambiguity resolution or other useful combinations available in the triple-frequency context.
Each independent phase bias can be directly estimated in a filter; however, in order to keep ascending compatibility with the dual-frequency case during the deployment phase of the new modernized satellites, we have chosen to stay in the old framework, that is, to work with combinations of biases. The resolution method is the following:
The widelane biases, that is, the identification of all thebLi – bLj quantities, are solved. For this computation and in order to have an accurate estimate of these biases, the two MW-widelane biasesµ12andµ15are used coupled to an additional phase bias, which is given by the triple-frequency ionosphere-free phase combination with the integer widelane ambiguities already fixed. This last combination using only phase measurements is much more accurate than MW-widelanes. The system to be solved is redundant and the noise of the different equations has to be chosen carefully.
The remaining bias (bLi ) is estimated using the traditional ionosphere-free phase combination of L1 and L2.
This computation has been implemented in the CNES real-time analysis center software, and since September 15, 2014, CNES broadcasts phase biases compatible with this triple-frequency concept on the IGS CLK93 real-time data stream.
Real Data Analysis
To prove the validity of the concept, at CNES, we compute several ambiguity combinations using real data. The process is the following:
Look for good receiver locations having a large number of GPS Block IIF satellites (transmitting the L5 signal) in view for a period of time exceeding 30 minutes, and choose among them, one participating in the IGS Multi-GNSS (MGEX) experiment. The station CPVG (Cape Verde) in the Reseau GNSS pour l’IGS et la Navigation (REGINA) network was chosen for the time span on September 28, 2014, between 19 and 20 hours UTC. During this period, four Block IIF satellites were visible simultaneously (PRNs 1, 6, 9, 30) for a total of 14 GPS satellites in view.
Record a compatible phase-bias stream. The CLK93 stream is recorded during the time span of the experiment.
Perform a PPP solution using the measurements, CLK93 corrections and biases to estimate the propagation distance, the troposphere delay and the receiver clock and phase ambiguity estimates according to Equation (5).
For different ambiguity estimates, compute and plot the obtained residuals.
We present in the following graphs various ambiguity residuals for the four Block IIF satellites in view. The values of each ambiguity are offset by an integer value for clarity purposes.
Melbourne-Wübbena Extra-Widelane.FIGURE 3 represents the MW extra-widelane (between frequencies L2 and L5) ambiguity estimation using our process. The MW extra-widelane ambiguity has a wavelength of 5.86 meters. The noise of the combination expressed in cycles is very low, and the integer nature of ambiguities in this combination is clearly visible.
FIGURE 3. Ambiguity residuals for the extra-widelane 5-2 combination.
Melbourne-Wübbena Widelanes.FIGURE 4 represents the MW-widelanes (the regular 1-2 and 1-5 combinations). Here again, the integer nature of the four ambiguities is clearly visible.
Widelane-Only Ionosphere-Free Phase. In the triple-frequency context, there is a possibility of forming an ionosphere-free combination of the three phase observables. This combination has an important noise amplification factor (>20), but would allow us to perform decimeter-accuracy PPP using only the solved widelane integer ambiguities and if the corresponding phase biases are accurate. In addition, it can be shown that the wavelength of the widelane ambiguity when the extra-widelane ambiguity is solved is about 3.4 meters. It means that the remaining widelane using this combination can be solved if the position is accurate enough (a few tens of centimeters) and the extra-widelane is known. FIGURE 5 shows such a case, that is, the residuals of the widelane ambiguity using this combination and assuming that the extra-widelane is already solved for.
FIGURE 5. Ambiguity residuals for widelane-only 1-2-5 ionosphere free combinations.
Such a case where the solution is the most biasedis shown (the dark blue curve). This behavior is mainly due to the difficulty in estimating the phase biases on this combination accurately using only a few Block IIF satellites. We hope that in the future the increasing number of modernized satellites will help such bias estimation.
N1 Ionosphere-Free Phase.FIGURES 6 to 8 show the three possible ambiguity estimates using the ionosphere-free phase combination with two measurements (we assume that the corresponding widelane has already been solved). In each case, the computed biases allow us to easily retrieve the integer nature of the N1 ambiguity.
FIGURE 6. Ambiguity residuals for the N1 combination using a fixed 1-2 widelane.FIGURE 7. Ambiguity residuals for the N1 combination using a fixed 1-5 widelane.FIGURE 8. Ambiguity residuals for the N1 combination using a fixed 2-5 widelane.
Application to Triple-Frequency PPP
The results presented above show that the integer ambiguity nature of phase measurements is conserved for various useful observable combinations and prove the validity of the model. Another experiment has been carried out to estimate the impact of ambiguity convergence in the triple-frequency context. For that, in order to maximize the observability of the GPS Block IIF constellation and thus the accuracy of the biases, a network of ten stations across Europe has been chosen for the phase biases computation (see FIGURE 9). The station REDU (in green) was the test station to be positioned. The test occurred on January 10, 2015, around 11:00 UTC. At that time, four Block IIF satellites were visible simultaneously (PRNs 1, 3, 6, 9) for a total of 10 satellites in view.
FIGURE 9. Network used for the triple-frequency PPP study.
The PPP-Wizard open source client was used to perform PPP in real time. The advantage of this implementation is that it directly follows the uncombined observable formulation described in Equations (5). The strategy for ambiguity resolution is a simple bootstrap approach.
Convergence of the Widelane-Only Solution. In this test, a PPP solution was performed, but only the fixing of the widelane ambiguities was implemented. As noted in the previous section, the wavelength of the widelane ambiguity when the extra-widelane ambiguity is solved is about 3.4 meters, so it is expected that all the widelanes can be fixed in a very short time. Despite the amplification factor of about 20 of the equivalent unambiguous phase combination, we expect to obtain an accuracy of about 10 centimeters with such a solution.
FIGURE 10 shows the convergence time of several PPP runs in this context (16 different runs of five minutes are superimposed), in terms of horizontal position error.
FIGURE 10. Widelane-only triple-frequency PPP convergence (horizontal position error).
The extra-widelanes are fixed instantaneously; the remaining widelanes are fixed in about two minutes on average to be below 30 centimeters (this is represented by the different sharp reductions of the errors). This new configuration, available in the triple-frequency context, is very interesting as it provides an intermediate class of accuracy, which converges very quickly and which is suitable for applications that do not demand centimeter accuracy. Another interesting aspect of this combination is the gap-bridging feature. In PPP, gap-bridging is the functionality that allows us to recover the integer nature of the ambiguities after a loss of the receiver measurements over a short period of time (typically a pass through a tunnel or under a bridge). This is done usually by means of the estimation of a geometry-free combination (ionosphere delay estimation) during the gap. Realistic maximum gap duration in the dual-frequency case is about one minute. In the triple-frequency case, the wavelength of the geometry-free combination involving the widelane (if the extra-widelane is fixed) is 1.98 meters. With such a large wavelength, the gaps are much easier to fill, and we can safely extend the gap duration to several minutes. In addition, the widelane combinations are wind-up independent, so there is no need to monitor a possible rotation of the antenna during the gap, as in the dual-frequency case.
Overall Convergence (All Ambiguities). Another PPP convergence test has been carried out with all ambiguities fixing activated (four different runs of 15 minutes are superimposed). Results are shown in FIGURE 11.
FIGURE 11. All ambiguities triple-frequency PPP convergence (horizontal position error).
The centimeter accuracy is obtained in this configuration within eight minutes, which is a significant improvement in comparison to the dual-frequency case. Further improvement of this convergence time is expected with an increase in the number of Block IIF satellites and, subsequently, GPS IIIA satellites.
Convergence Time Comparison Between the Dual- and Triple-Frequency Contexts. Thanks to these new results, a realistic picture for PPP convergence in the dual- and triple-frequency contexts can be drawn. To do so, polynomial functions have been fitted over the data points obtained in the previous studies. Two data sets were used:
Standard dual-frequency convergence (GPS only, 10 satellites in view).
Triple-frequency convergence (GPS only, 10 satellites in view, four Block IIF satellites).
FIGURE 12 represents the comparison between the two polynomials (horizontal error).
FIGURE 12. Realistic PPP convergence comparison between dual- and triple-frequency contexts (horizontal position error).
Conclusion
The new phase-bias concept proposed for RTCM SSR has been successfully implemented in the CNES IGS real-time analysis center. This new concept represents the phase biases in an uncombined form, unlike the previous formulations. It has the advantage of the unification of the different proposed methods for ambiguity resolution, and it prepares us for the future; for example, for a widely available triple-frequency scenario. The validity of this concept has been shown; that is, the integer ambiguity nature of phase measurements is conserved for various useful observable combinations.
In addition, we have also shown that the triple-frequency context has a significant impact on ambiguity convergence time. The overall convergence time is drastically reduced (to some minutes instead of some tens of minutes) and there is an intermediate combination (widelane-only) that has some interesting properties in terms of convergence time, accuracy and gap-bridging for non-demanding centimeter-level applications.
Acknowledgments
The contributions of colleagues contributing to the IGS services are gratefully acknowledged. Geo++ is thanked for useful discussions on the standardization of phase bias representation.
DENIS LAURICHESSE received his engineering degree and a Diplôme d’études appliquées (an advanced study diploma) from the Institut National des Sciences Appliquées in Toulouse, France, in 1988. He has worked in the Spaceflight Dynamics Department of the Centre National d’Etudes Spatiales (CNES, the French Space Agency) in Toulouse since 1992, responsible for the development of the onboard GNSS Diogene navigator. He was involved in the performance assessment of the EGNOS and Galileo systems and is now in charge of the CNES International GNSS Service real-time analysis center. He specializes in navigation, precise satellite orbit determination and GNNS-based systems. He was the recipient of The Institute of Navigation Burka Award in 2009 for his work on phase ambiguity resolution.
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“Integer Ambiguity Resolution on Undifferenced GPS Phase Measurements and Its Application to PPP and Satellite Precise Orbit Determination” by D. Laurichesse, F. Mercier, J.-P. Berthias, P. Broca, and L. Cerri in Navigation, Journal of The Institute of Navigation, Vol. 56, No. 2, Summer 2009, pp. 135–149, doi: 0.1002/j.2161-4296.2009.tb01750.x.
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“The CNES Real-time PPP with Undifferenced Integer Ambiguity Resolution Demonstrator” by D. Laurichesse in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of The Satellite Division of the Institute of Navigation, Portland, Ore, September 20–23, 2011, pp. 654–662.
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Improved Convergence for GNSS Precise Point Positioning by S. Banville, Ph.D. dissertation, Department of Geodesy and Geomatics Engineering, Technical Report No. 294, University of New Brunswick, Fredericton, New Brunswick, Canada. Recipient of The Institute of Navigation 2014 Bradford W. Parkinson Award.
NovAtel has announced significant performance improvements to its precise point positioning (PPP) solution. NovAtel CORRECT with PPP will now offer the new TerraStar-C correction service as its exclusive source for satellite-delivered PPP correction data.
TerraStar-C contains an enhanced correction data set that enables up to 4-centimeter accuracy and instant re-convergence when combined with the receiver error models and positioning algorithms offered by NovAtel CORRECT. This new level of PPP performance is available on NovAtel’s OEM6 receivers with firmware version 6.600.
NovAtel CORRECT is the positioning algorithm on NovAtel’s GNSS receivers that handles corrections from a variety of sources, including real-time kinematic (RTK), PPP, spaced-based augmentation systems (SBAS) and differential GPS (DGPS). NovAtel CORRECT with PPP combines GNSS satellite clock and orbit correction data from TerraStar’s global reference station network with NovAtel’s receiver algorithms to yield robust sub-decimeter positioning without the need for nearby base stations.
Correction data provided by TerraStar is delivered to the end user via Inmarsat satellites. With satellites visible globally, PPP is an ideal solution for precision applications where communications infrastructure is either unreliable or not available. In addition, applications where signal interruptions are common will benefit from a more robust positioning solution with the ability to quickly regain full accuracy following a temporary loss of GNSS signals, NovAtel said.
NovAtel customers with current TerraStar-D subscriptions have the option to upgrade to the new TerraStar-C service level free of charge. The new NovAtel PPP performance level is available immediately.
Veripos, a supplier of high-precision GNSS positioning facilities to offshore and associated industries, has upgraded its Apex and Apex² precise point positioning (PPP) services.
The services now typically provide users with a horizontal position accuracy of better than 5 cm and 12 cm in the vertical at the two sigma (95 percent) confidence level.
The enhanced levels of performance, now available to all current users of Apex and Apex² without any need to upgrade, have been calculated from static data obtained in Aberdeen, Scotland, where Veripos is headquartered, as well as Houston, Texas, and Singapore. Veripos said that accuracies may vary with observing conditions, however.
The improved accuracy follows a major upgrade of the entire Veripos global reference station network for tracking all GNSS signals, together with introduction of new receivers and geodetic antennas for delivery of better measurement quality resulting from refinements to algorithms and software used to derive necessary GNSS orbit and clock corrections.
Designed to meet all offshore positioning and navigation applications, the dual-beam Apex and Apex² PPP services are relayed via seven geostationary communications satellites to ensure continuous availability and service redundancy while providing access to both GPS and GLONASS constellations. Positional accuracy is maintained regardless of user location, Veripos said.
A new version of the online GAPS precise point positioning software is now available. GAPS — GPS Analysis and Positioning Software — is offered by the University of New Brunswick Geodesy and Geomatics Engineering Department.
The latest release provides capabilities for handling GPS data files in both RINEX 2 and 3 formats, whether Hatanaka-compressed or not, along with a number of receiver raw file formats. Also, additional input and output data-quality verification is now performed.
More information on the release can be found here, and the new version is available here.
A new version of the open source PPP-Wizard user software has been published. The link to download the wizard is being provided on request, so the International GNSS Service (IGS) can keep track of interested users.
The PPP-Wizard is defined as a precise point positioning with integer and zero-difference ambiguity resolution demonstrator. Available for non-commercial purposes, it performs real-time PPP using corrections streams provided by the IGS Real Time Service. It features:
GPS and GLONASS code and phase measurements (mono or dual frequency)
Ambiguity resolution on GPS thanks to the new standardized phase biases messages (with the compatible CLK91 stream)
Advanced RAIM
SBAS iono for single-frequency receivers (u-blox and nvs receivers)
Fast reconvergence using iono estimation
Compatibility with rtklib and BNC (rtrover interface)
Multiple receivers processing
Preparation for augmented regional networks (tropo & iono interface)
C/C++ portable and light implementation
The PPP-Wizard demonstrator is a “proof of concept” of the zero-difference ambiguity resolution method developed in the orbit determination service at CNES.
NovAtel, Inc., OEM provider of high-precision GNSS positioning products, has launched its NovAtel Correct positioning technology. NovAtel Correct optimally combines data from multiple GNSS satellite constellations with corrections from a variety of sources, to deliver the best position solution possible.
NovAtel Correct provides integrators with the opportunity to choose pricing and subscription options that best match their OEM business objectives. Delivery of correction data is available via satellite or Internet, depending on the requirements of the application. With NovAtel in control of the entire positioning solution, future innovation including seamless integration with all positioning modes and correction types is assured.
Designed for NovAtel’s OEM6 high-precision receivers, the NovAtel Correct precise point positioning (PPP) solution delivers decimeter-level accuracy worldwide. L-band delivered PPP corrections from TerraStar are supported by NovAtel Correct without users having to add base-station infrastructure. Developers of land, airborne and near shore applications can purchase subscriptions to TerraStar’s correction service directly through NovAtel.
“For a number of reasons, many of our customers have been eager for an end-to-end NovAtel OEM positioning service,” said Jason Hamilton, VP, Marketing for NovAtel. “NovAtel Correct rounds out our product and service offering and gives customers one-stop shopping for receivers, antennas and correction services.”
Satellite and NTRIP-based solutions will be available for OEM6 products in Q1 2014 for all applications requiring decimetre-level positioning.
RTKLIB, a developer of open source software for standard and precise GNSS positioning, has released its latest RTKLIB software (version 2.4.2), which fully supports NVS Technologies’ BINR proprietary binary protocol and the NV08C GNSS receiver series.
The use of RTKLIB, in conjunction with NVS Technologies’ NV08C GNSS receiver series, including the highly integrated NV08C-CSM surface mount module with geodetic grade raw data output, enables GNSS system designers and OEMs to develop highly accurate, low cost and compact precision-grade positioning and navigation equipment.
RTKLIB features include:
Full compatibility with NVS Technologies’ NV08C Series GNSS Receivers.
A portable program library and several APs.
Standard and precise positioning algorithms using GPS, GLONASS, Galileo, QZSS, BeiDou and SBAS.
Supports various GNSS based positioning modes, both for real-time and post-processing, including: Single, DGPS/DGNSS, Kinematic, Static, Moving-Baseline, Fixed, PPP-Kinematic, PPP-Static and PPP-Fixed.
Positioning mode for real‐time and post‐processing, including Single, SBAS, DGPS, RTK, Static, Moving‐base and PPP.
Supports many standard formats and protocols for GNSS, including RINEX 2 & 3, RTCM 2 & 3, BINEX, NTRIP 1.0, RTCA/DO-229C, NMEA 0183, SP3-c, ANTEX 1.4, IONEX 1.0, NGS PCV and EMS 2.0.
External communication via Serial, TCP/IP, NTRIP, local log file (record and playback) and FTP/HTTP (auto download).
Contact NVS Technologies for specific features compatibility. Visit www.rtklib.com for RTKLIB’s latest (ver. 2.4.2) software package download, release note, information, tutorial, manual and support.
Fugro Seastar AS has been looking forward to demonstrating Real-Time Precise Point Positioning (PPP) based solely on Galileo signals since the last two satellites were launched October 12, the company said.
Those two satellites brought the constellation to a total of four satellites, the minimum required to permit calculation of a Galileo-only position. Fugro achieved this task on March 18, within a week of all four Galileo satellites being activated. Fugro is now generating Galileo orbit and clock corrections, which can be used in conjunction with the Fugro G2 decimeter-level corrections associated with its GPS/GLONASS PPP service.
The plot below shows performance of the Fugro orbit and clock service using GPS, GLONASS and Galileo satellites between 06:00 and 08:00 UTC, March 18, 2013, in Oslo, Norway. Between 07:00 and 07:30 UTC, only the four Galileo satellites were used for the solution, which achieved a similar accuracy to Fugro’s existing service, the company said.
“It is interesting that the noise level of the position is better with Galileo alone than when GPS and GLONASS satellites are also used,” Fugro said in a statement. “This is very encouraging as with only four satellites to choose from, the geometry of the Galileo-based solution is much weaker than the solutions before and after the Galileo-only period. This performance exceeds our expectations and suggests a strong future for Fugro’s Galileo PPP solution.”
It used to be that professionals using precision GNSS applications had to go to the expense of buying, operating, and maintaining RTK base-stations and radio set-ups. Then L-Band corrections came on the scene and things changed. Most precision receiver manufacturers supply an L-band option for a nominal fee, and also sell PPP service subscriptions. There are now a number of PPP correction service providers offering higher precision, including a couple of new options.
As a quick overview — L-band is just like WAAS, but with privately owned assets, rather than provided by a state agency. WAAS focuses on high integrity and accuracy, while L-band corrections are largely more focused on providing accuracy to users. A geographically distributed ground network of base stations sends receiver data to one or more central processing facilities, which formulate wide area corrections. A number of uplink stations then send these corrections up to geostationary satellite transponders (time on a number of satellites is often rented, but L-band companies could also own and operate their own satellites), and the transponders transmit the wide area corrections at L-band frequency for reception by suitably configured user receivers. Users are able to buy subscriptions that enable them to receive corrections for a period of time — and that’s how the private L-band suppliers make money. The accuracy a user can achieve depends on the service, but anything from a few meters to a few centimeters is now possible.
Before WAAS was fully operation in the U.S., L-band corrections supplied by private companies were already available. It became possible to regularly get meter-level accuracies without base-stations, and it was clear that this could well turn into a major benefit for users. Operations like agricultural automation, asset tracking, mining, marine navigation, and others that could get by with a few meters of accuracy began to rely on L-band corrections. Geographic Information Systems (GIS) could even work without base stations, and vehicle tracking could determine which side of the road a truck was on. Then with expanding worldwide ground networks, more satellites and ever-improving clock and orbit algorithms, we started talking about corrections that gave us decimeter accuracies. That’s when PPP began to outpace WAAS for some applications requiring higher precision.
Never quite got the significance of why the original marine PPP companies were spinning off land-focused providers from their marine businesses, but the original marine correction providers now have successfully established “land-only” provider companies. It makes sense to have a supplier talk to you in marine terms if you’re running a shipping company, and for that provider to focus on providing higher integrity corrections to your shipping fleet. Land-based machine control, GIS and vehicle tracking outfits, on the other hand, want their own land-based support networks and don’t want to talk in marine terms. So we now have a number of providers supplying different sets of PPP corrections. It’s also possible that segment pricing for the different markets might have played a role in these spin-offs.
The granddaddy system would seem to be Fugro’s OminSTAR — whose services are now marketed by Trimble following acquisition of OminSTAR marketing rights by Trimble in 2011, while Fugro retained its marine services. OminSTAR HP, G2, XP and VBS services are available courtesy of a worldwide network of reference stations, data networks, carrier-phase measurements and sophisticated “clocks and orbits” correction algorithms which provide sub-meter thru 10-cm capability to users.
The OmniSTAR network.
And of course Trimble is also running its own RTX service alongside OmniSTAR. With a world-wide reference station network, and a number of concentrated regional networks, CenterPoint RTX is regularly achieving less than 4cm for users. RTX is available over regular L-band satellite and over internet. Overall an impressive PPP capability.
The CenterPoint RTX network, by Trimble.
Then NavCom — and Deere & Co, its parent company — fielded the StarFire system for both NavCom and John Deere customers, who not surprisingly use it mainly for agriculture. However, use of the system has grown since it was introduced in 1999 and currently around 10 percent of customers are in markets other than ag — in offshore, survey, construction, aerial, GIS, and government/military applications. The StarFire signal is available worldwide but NavCom offers two differently priced services: “Land Only” and “All Area” for non-ag applications. You have to have Navcom or John Deere equipment to be able to use it, but the network and the receivers come from the same people, so the system has been optimized for peak performance and there shouldn’t be concerns about third-party integrators or service providers.
In 2001 in collaboration with JPL, Real Time GIPSY (RTG) was combined with the existing StarFire clocks and orbits algorithms and a StarFire GPS 10-cm service was offered. Nowadays StarFire GNSS has evolved out of that original correction service and claims impressive 5-cm accuracies using its multi-constellation GPS and GLONASS corrections.
The Starfire GNSS network.
StarFire also uses over 80 reference stations with mostly GPS/GLONASS receivers providing carrier phase data for redundant processing and distribution by L-band transmissions over the Inmarsat satellite network.
Then we come to the latest entrant into the land PPP business – TERRASTAR. The parent company Veripos has been around since 1989 and has been extremely successful in its marine business, going public in 2012 on the Oslo stock exchange. Veripos recently launched TERRASTAR to better address the land market for all the same good reasons discussed earlier. TERRASTAR provides two correction services: –M is meter level DGNSS and –D is a decimeter solution using both GPS and GLONASS. All the 80+ owned and operated reference sites around the world have dual-frequency GPS/GLONASS receivers, and there are plans to add Galileo and even COMPASS in the future.
Dual-redundant processing and network servers ensure uninterrupted distribution of GPS and GLONASS orbit and clock corrections, enabling decimeter accuracy for users. TERRASTAR distributes corrections over all seven Inmarsat GEOs, providing most land users with redundant L-band visibility.
Correction quality and availability are largely dependent on the number of reference stations that track the same GNSS satellite. The figures below show the location of satellites at a given time and the number of stations simultaneously tracking those satellites. For the TERRASTAR ground network, there are often more than 30 stations tracking the same satellite. This makes for high-quality clock and orbit corrections, and TERRASTAR-D claims to provide consistent, stable horizontal 5-10 cm and vertical 10-15 cm performance.
The TERRASTAR network.
As a new player, TERRASTAR has yet to corner a whole bunch of customers, but it already has some significant customer applications. It “GEO-Gates” its corrections like other providers to ensure usage on land, but it extends coverage to land areas plus about 6 km beyond the coastline — termed “nearshore.” So TERRASTAR has been able to capture in-shore dredging and construction business in Europe that otherwise might have had to go to more expensive marine correction services.
In addition, a new customer is using TERRASTAR for airborne geophysical applications. There are also ongoing trials on excavators in road construction, on trains, in oil and gas, for GIS/surveying, and with integrated agricultural sprayer-control and harvesters. TERRASTAR plans shortly to offer a web-based e-Commerce System for users to control their subscriptions. TERRASTAR and Septentrio/Altus have long-term relationships for receivers/systems, and Septentrio and Altus also retail the TERRASTAR service.
So, just when you think you have a good picture of PPP, another option for users has started to show up. PPP over internet — or iPPP as Nexteq Navigation in Calgary, Canada terms their service – is designed to provide similar corrections as PPP, but over cellular phone or Wi-Fi connection to the internet, rather than over satellite. With single frequency GPS, Nexteq claims accuracies of around 50cm, and 10cm with dual frequency, although their T5 and T5A handhelds only currently support L1. Of course Trimble has had corrections over the internet for a number of years.
So its clear that PPP services continue to evolve and become more and more sophisticated to match the growing complexity of customer applications. And as achievable accuracies improve, we’re seeing use in higher precision applications which would have seemed impossible just a few years ago, where local RTK base-stations and radio links would have been the only way to go.
With several very capable sources to choose from, GNSS industry customers have several options to carefully assess and fit to their business. Each PPP supplier has specific advantages and features available to meet customer expectations. The market now appears to be large and specialized enough that its inviting for new entrants. And each new entrant seems to bring with them new twists and capabilities which sell their services. As a customer, it’s a good time to trial new precision applications with PPP.
A new method brings together advantages of real-time kinematic (RTK) and precise point positioning (PPP) in a technique that does not require local reference stations, while still providing the the high productivity and accuracy of RTK systems with the extended coverage area of solutions based on global satellite corrections. The real-time centimeter-level accuracy without reference-station infrastructure is suitable for many market segments — and is applicable to multi-GNSS constellations.
By Rodrigo Leandro, Herbert Landau, Markus Nitschke, Markus Glocker, Stephan Seeger, Xiaoming Chen, Alois Deking, Mohamed Ben Tahar, Feipeng Zhang, Kendall Ferguson, Ralf Stolz, Nick Talbot, Gang Lu, Timo Allison, Markus Brandl, Victor Gomez, Wei Cao, and Adrian Kipka
Real-Time eXtended (RTX) positioning is a technology produced by combining a variety of innovative techniques, which together provide users with centimeter-level real-time position accuracy anywhere on or near the Earth’s surface. This new technique is based on the generation and delivery of precise satellite corrections (that is, orbit, clocks, and others) on a global scale, either through a satellite link or the Internet. The innovative aspects of the new solution can be divided into different categories, which directly relate to the areas that have previously limited the provision of global high-accuracy positioning:
Because of various new aspects of the technique, RTX differs from both differential RTK and precise point positioning as currently understood by the general GNSS community.
System Overview
RTX technology is used to provide centimeter-level GNSS positioning through the CenterPoint RTX service. Figure 1 shows the general infrastructure of the system.
Data from monitoring stations distributed around the globe are collected and transmitted via the Internet to operation centers at different locations. The complete operation centers (enclosed by the red dashed square) are redundant in order to assure the very high (~100 percent) availability of the system. In case it is needed, the correction stream source might change between operation centers and/or processing servers within centers. These operational changes are handled in a deterministic way by all parts of the system including the user receiver. Inside the operation centers, redundant communication servers relay the network observation data to the data processing servers, which host the network processors that produce precise orbit, clock, and observation biases valid for any place on the globe.
After being generated, the precise satellite data are compressed in messages compliant with the CMRx format, specially developed for compact transmission of satellite information. The messages are finally routed to either a satellite uplink station or made available for Internet connection access by the users.
The CenterPoint RTX tracking network currently consists of around 100 stations, distributed across the globe, as shown in Figure 2. The CenterPoint RTX service is currently offered in North and South America, via satellite link, as indicated in Figure 3. Today the CenterPoint RTX service has been made available globally for all those with Internet access.
FIGURE 2. CenterPoint RTX tracking network distribution. (Click to enlarge.)FIGURE 3. CenterPoint RTX L-band satellite service coverage in the Western hemisphere.
Limiting Factors
To understand the limiting factors associated with global high-accuracy positioning, it is helpful to consider the simplified basic GNSS observation equations for carrier-phase and code measurements:
Φi=ρ+c(dT−dt)+T−Ii+λi Ni,
+Ai−ai+λi(WΦ−wΦ)+BΦ,i−bΦ,i+MΦ,i+nΦ,i
and
Pi=ρ+c(dT−dt)+T+Ii,
+Ai−ai+BP,i−bP,i+MP,i+nP,i
where:
Φi is the carrier-phase measurement for frequency i in meters;
ρ is the geometric distance between the antennas of the receiver and satellite in meters;
c is the speed of light constant in meters per second;
dT is the receiver clock error in seconds;
dt is the satellite clock error in meters per second;
T is the slant neutral atmosphere delay in meters;
Ii is the ionospheric delay for frequency i in meters;
λi is the carrier-phase wavelength for frequency i in meters;
Ni is the integer carrier-phase ambiguity for frequency i in cycles;
Ai is the combined receiver antenna offset and directional variation correction for frequency i in meters;
ai is the combined satellite antenna offset and directional variation correction for frequency i in meters;
WΦ is the receiver antenna phase wind-up effect, in cycles;
wΦ is the satellite antenna phase wind-up effect, in cycles;
BΦ,i is the carrier-phase receiver bias for frequency i in meters;
bΦ,i is the carrier-phase satellite bias for frequency i in meters;
MΦ,i is the carrier-phase multipath for frequency i in meters;
nΦ,i is the carrier-phase observation noise and other un-modeled effects for frequency i in meters;
Pi is the pseudorange measurement for frequency i in meters;
BP,i is the pseudorange receiver bias for frequency i in meters;
bP,i is the pseudorange satellite bias for frequency i in meters;
MP,i is the pseudorange multipath for frequency i in meters;
nP,i is the pseudorange observation noise and other un-modeled effects for frequency i in meters.
The feasibility of high-accuracy absolute positioning relies on the assumption that phase and code measurements on the different frequencies or on specific observation combinations are modeled quite reliably. This ultimately means that the parameters (or certain combination of them) of the two equations given are known very precisely, that is, with an accuracy of better than a few centimeters.
Having a global system where every component of the un-differenced GNSS observational model is well known requires advanced understanding and modeling of the involved GNSS-related effects. This is a general achievement of the RTX system.
(An extensive section here, encompassing satellite orbits and clocks, receiver clock error, antenna phase center odeling, phase wind-up effects, neutral atmosphere delay, and ionospheric delay, appears in the online version of this article, at env-gpsworld-integration.kinsta.cloud/rtx.)
Real-Time Network Processing
As previously stated, the RTX system works based on precise satellite information generated at processing centers and broadcast to users. The precise information employed by the systems comprises satellite orbits, satellite clocks, satellite biases, and other auxiliary information.
The requirements for the satellite orbits to be used in the global RTX system can be summarized as accuracy, continuity, robustness, and reliability. The satellite positions have to be accurate for obvious reasons, including the fact that orbit errors have direct impact on rover-position determination quality. Furthermore, because the RTX network process algorithms use ambiguity resolution, the reliability of the ambiguity determination is highly affected by the satellite orbits quality due to the distances between reference stations in the tracking network. The continuity requirement is put in place to avoid the need of handling observation modeling inconsistency over time for both network and rover processing.
For the same reason, the overall system employs techniques to properly handle switches between redundant orbit-processing servers without degradation of position quality. As one would expect, network processors have to be, in general, robust against the eventuality of poor data entering the system for various reasons. The RTX network processors employ a variety of quality-control techniques to ensure that only data with the highest expected quality is used for the computation of end products.
Finally, reliability is a very important factor for real-time orbit processing. At the current stage, the RTX real-time orbit processors are able to run for several months with virtually zero intervention from operators, while handling events such as satellites going through unhealthy periods and satellite maneuvers (during unhealthy period or not).
There are at least two strong reasons for justifying the need of implementing and running an RTX proprietary orbit processing server. The first one is simply the need of reliably meeting the above-mentioned requirements. The second one is that from an operational perspective, the RTX system is conceived in such a way that it does not rely on any external source of information to run at its full accuracy capability. Figure 4 shows the achieved orbit errors provided by IGS ultra-rapid products during two weeks of March 2011, where IGS rapid orbit products are used as truth. The ultra-rapid orbits are evaluated using the initial portion of the predicted arc, thus making use of the most reliable part of the predicted arcs as the products become available in real-time. In that case, neither accuracy nor continuity requirements for RTX processing are completely met.
FIGURE 4. IGS ultra-rapid orbit errors, as compared to IGS rapid orbit products.
Orbit Estimation. The orbit estimation in the CenterPoint RTX system is based on a combination of a UD-factorized Kalman filter estimating satellite position, satellite velocity, troposphere states, integer ambiguities, solar radiation pressure parameters, harmonic coefficients, and Earth-orientation parameters. The prediction step in the filter uses a numerical integration of the equations of motion in connection with a dynamic force modeling. Forces considered in the approach are: the Earth’s gravity field, lunar and solar direct tides, solar radiation pressure, solid earth tides, ocean tides, and general relativity.
In RTX orbit processing carrier phase integer ambiguities are resolved in real-time. Also, the satellite orbit states are truly estimated in real-time and continuously adapted over time to better represent the current reality. This means that the satellite positions that are evaluated by the user have prediction times of no more than a few minutes since the last orbit processing filtering update, providing negligible loss of accuracy. Figure 5 shows the orbit errors obtained from the RTX orbit processor. Similarly to the previous figure, IGS rapid orbit products are used as reference. The time span is also the same as in the previous figure. The RTX real-time orbit components have a typical overall accuracy of around 2.5 centimeters (cm), and a 3D error accuracy of around 4 cm, considering IGS rapid products as truth.
FIGURE 5. RTX real-time orbit errors, as compared to IGS rapid orbit products.
Clock Estimation. Satellite clock estimation forms an essential part of the RTX system. It plays a fundamental role on positioning performance due to a number of reasons. Satellite clocks map directly into line-of-sight observation modeling, yielding into a one-to-one error impact from clocks into GNSS observables modeling. Due to the same strong relationship, it is of fundamental importance that clocks are generated in a way to facilitate ambiguity resolution within the positioning engine. The processing speed of a clock processor is also of critical importance, due to the fact that any delay in computing satellite clocks is directly translated into correction latencies when computing real-time positions on the rover side. For that matter, one should keep in mind that regardless how late satellite corrections get to the GNSS receiver in the field, positions have to be provided to the user as soon as the rover GNSS measurements are available. Therefore latencies typically introduce errors into the final real-time position. In this article, we define real-time positioning as the computation of positions at the time when the rover observables are available, regardless of the latency of the correction stream. This is a necessary concept in order to support dynamic rover GNSS positioning.
The RTX clock network processor was designed around the requirements discussed earlier. It computes clocks that are compatible with ambiguity resolution on the user receiver. As a matter of fact, the clock network processor itself employs ambiguity resolution for the generation of the RTX clocks. The processor architecture is based on an innovative design that allows processing data of several hundreds of reference stations, including all necessary steps such as data quality control, ambiguity resolution, and the final clock generation, within a fraction of second. The processing time of this kind of real-time network processor has to be minimized as much as possible in order to allow the processor to operate at 1 Hz, and to minimize the final correction latency at the rover end. Note that the final latency of the correction stream is a composition of three basic components: the time for the network data to arrive at the network processing server; the network processing time; and the correction transmission time to reach the final user. Figure 6 shows the typical total correction latency for the RTX system, when corrections are broadcast through a satellite link.
FIGURE 6. Typical RTX correction stream latency. The dashed green line represents the latency at 50 percent (3.7 s), and the dashed red line represents the latency at 99 percent (5.6 s).
Unlike satellite orbits, satellite clock solutions are more difficult to compare directly. This is because different clock solutions might have offsets between each other, as well as behave differently due to differences in their GNSS reference time realization process as well as in their observation modeling approaches. That said, one way of verifying the quality of satellite clocks is to quantify how well it can be used to model actual receiver observation data. This can be in general achieved by applying satellite orbit and clock correction onto GNSS data and verifying the remaining residuals. Other quantities such as receiver coordinates have to hold their correct values for the residuals to be meaningful. In this case, the combined satellite orbit and clock error are assessed, and not just the satellite clock alone. For our purposes this is perfectly fine, since this is the way orbits and clocks are employed in rover positioning as well. Figures 7 and 8 show typical combined satellite orbit and clock errors at line-of-sight for different satellites. Figure 7 shows the ionospheric-free phase modeling error for GPS satellites, while Figure 8 is for GLONASS. Note that observations of a reference satellite (highest elevation at the time of observation) were reduced from the others. This was done in order to remove the receiver clock errors from the residuals. For both GPS and GLONASS cases, the observation modeling error after using RTX orbit and clock corrections is on average at the 1 cm level, with values typically less than 2 cm. The GPS satellite with outlying behavior in the plot below was setting at that time, and the increased amplitude of the residuals is mostly due to receiver observation errors such as multipath.
FIGURE 7. RTX clock quality (GPS) by means of corrected ionospheric-free phase measurements.FIGURE 8. RTX clock quality (GLONASS) by means of corrected ionospheric-free phase measurements.
Communication and Positioning
Once all satellite information is available, it must be compressed in a message that can be broadcast to the user in the field. The transmission of global corrections can be done in different ways, such as via Internet, in case the user has access to it, or using a satellite link. In the latter it is customary that corrections sufficient to cover the transmission satellite footprint are broadcast, rather than corrections complete enough to cover the globe. Firstly, because it is expected that users operating inside the satellite footprint will use the corrections only for that region, and secondly because bandwidth restrictions usually play a role in message design for satellite-based communication. The bandwidth restrictions not only enforce maximum bandwidth utilization below a certain limit, but also require that the utilization over time is homogeneous to ensure optimal usage of the satellite channel.
Furthermore, satellite signals are typically susceptible to frequent message-packet losses depending on the user environment, such as when a receiver is running under canopy. To mitigate packet losses, the message must be built in such a way as to allow the rover to continue operations with minimum loss of availability. In that case not only the message design has to foresee this type of situation, but also the message decoding, usage, and positioning algorithms have to be optimized to most favorably couple with the received messages. All these factors have been taken into account in RTX system communication design. A new message format was created to carry information on satellite orbits, clocks, observation biases, and other auxiliary information. The new RTX CMRx satellite messages deliver 1-millimeter resolution for satellite orbits and clocks.
The RTX positioning engine inherits several technological aspects from Trimble’s pre-existing RTK engine. This aspect makes the RTX positioning mode, and traditional RTK positioning modes (for example, single base, virtual reference station) easy to co-exist. Among other things, the new engine has been thoroughly tested and optimized for challenging tracking environments. In these scenarios the engine is presented with observation data collected with a high level of multipath and low signal-to-noise ratio, often producing cycle slips and gaps in the data. As previously mentioned, at the same time the correction stream also suffers packet losses and the correction data might not be completely available during certain masking conditions.
Positioning Performance. The RTX engine delivers typical final accuracies at 1–2 cm level for horizontal positioning, and 2–4 cm for vertical, 1-sigma. The final convergence of the system is achieved in 10 to 45 minutes after receiver startup. The time to converge might depend on several aspects, including satellite geometry and multipath conditions.
To overcome the increased convergence time as compared to traditional RTK systems, a number of features have been implemented as part of the RTX positioning engine, two of which are worthy of mention here. The Fast Restart feature allows users to power up or place the receiver at a known location and immediately obtain a converged solution. This is also applicable when users have not moved their equipment since the last RTX solution. This feature is quite valuable in agriculture applications, where the user typically does not move the tractor between RTX-steered field work activities, thus avoiding in the majority of cases the need to wait through a new convergence period before starting work, one or more days after the last system usage.
The second feature is also related to avoiding system re-convergence. The Bridging feature, an outage recovery capability, enables the RTX positioning engine to immediately recover from a complete constellation outage with loss-of-lock during any dynamic activity. This prevents the system from entering a new convergence phase in case the receiver loses track of up to all satellites in view, coupled with outages of up to a couple of minutes, such as when running behind a tree line, or under a bridge.
Accuracy
Horizontal position error obtained in real time in a receiver acquiring the RTX correction data through the satellite link in North America is shown in Figure 9. The receiver was running continuously for several days, and was located in Ames, Iowa. As displayed, the horizontal RMS was 1.4 cm, with a 95 percent horizontal error of 2.4 cm. These are typical values for satellite-based RTX horizontal performance.
FIGURE 9. RTX real-time horizontal positioning performance. Results obtained from a receiver operating in Ames, Iowa.
Figure 10 shows the vertical performance for the same receiver and time period: the vertical RMS was 2.8 cm, with 95 percent vertical error of 4.4 cm.
Time to Achieve Convergence. Convergence is directly connected to the level of productivity that can be achieved for actual field applications. In the following example a continuously powered RTX receiver was used to show an assessment of the RTX (re-)convergence capability. The receiver’s tracking of all satellites was disabled every hour by an antenna switch. Each outage lasted three minutes, during which times no GNSS satellites were tracked.
FIGURE 10. RTX real-time vertical positioning performance.Results obtained from a receiver operating in AMES, Iowa, US.
This procedure was repeated hourly for several days in order to gather enough performance runs to derive meaningful statistics. Figure 11 shows the resulting performance of this assessment. The standard cold-start re-convergence performance is indicated with blue lines, where the solid lines represent 90-percent performance and the dashed line represents 68-percent performance.
As the figure shows, the RTX system converged to better than 5 cm horizontal error after 20 and 25 minutes for 68 percent and 90 percent of the runs, respectively. Convergence time is correlated with a number of aspects, including satellite geometry and multipath environment. Because of these variations, the claimed RTX convergence time is between 10 and 45 minutes for full accuracy achievement.
The red lines in Figure 11 indicate performance obtained with a second receiver, connected to the same antenna, and thus subject to the exactly same GNSS signal outages. This second receiver had the Bridging functionality enabled, and thus is expected to bridge the outages and phase cycle slips without resetting the positioning solution. The red lines confirm that the desired behavior is achieved. To better visualize what happens over time in this case, Figure 12 shows a few hours of the real-time results obtained with the receiver running with the Bridging functionality activated.
Figure 13 gives an example of Internet protocol (IP)-based RTX performance. This is a single run where the system converged to better than 5 cm (horizontal) in approximately 15 minutes. Figure 14 shows how the L1 ambiguities of individual satellites in view during that time converged.
FIGURE 13. RTX IP-based run example.FIGURE 14. Example of ambiguities convergence during an RTX IP-based run.
In these two plots, positioning convergence is, as expected, highly correlated with the ambiguities convergence to their final integer values in cycles. Note that satellites that come in after the overall solution is converged (for example, in light blue) achieve their final ambiguity values much quicker than during the position convergence phase, also as expected. The proprietary algorithms used for ambiguity resolution and validation in RTX allow the ambiguities to reliably converge to their integer values. Arbitrary integer number of cycles have been removed from the original ambiguity values to allow better simultaneous visualization of the ambiguities for several satellites.
Optimizing the RTX system to work under different scenarios was necessary because the multipath and signal availability levels are reasonably different between running an antenna with a reference station setup and the actual user environment, where the data tracking conditions impose additional challenges on making high-accuracy positioning effective on a global basis, in a productive manner. Therefore, an extensive field test campaign was conducted during the pre-release phase of the RTX system. The next example shows RTX in-field performance for an precision agriculture application in Illinois. The setup is typical for agricultural use, with the antenna and receiver mounted on a tractor that ran for about 103 minutes. Figure 15 shows theactual track of the tractor; RTX corrections were received via satellite link.
FIGURE 15. RTX tractor field test track in Illinois.
The horizontal positioning performance for that field test can be seen Figure 16. The overall 2D RMS was 2.3 cm and the 95 percent horizontal error was 4.2 cm. Note that this position difference plot is between the RTX solution and a short-range single baseline (SBL) RTK solution providing truth. Therefore the numbers and plot actually show a combination of errors between the global RTX solution and the SBL solution to the local reference station.
FIGURE 16. Horizontal positioning results for a real-time RTX tractor field test in Illinois.
Nevertheless the error magnitudes achieved lie within the same range as in the previous assessments shown here.
Summary
RTX positioning brings together the advantages of positioning techniques that do not require local reference stations while providing the productivity of RTK positioning. Its deployment introduces innovations in GNSS network processing, as well as advancements in the rover global positioning algorithms.
RTX employs ambiguity resolution on a global scale for both network and rover processing, including GPS and GLONASS satellites in the solution. The delivery of this new technology is achieved through the CenterPoint RTX positioning service, capable of providing world-wide real-time centimeter-level accuracy without the direct use of a reference station infrastructure.
A longer version of this article was presented at the 2011 ION-GNSS conference in Portland, Oregon.
Rodrigo Leandro, Herbert Landau, Markus Nitschke, Markus Glocker, Stephan Seeger, Xiaoming Chen, Alois Deking, Mohamed Ben Tahar, Feipeng Zhang, Kendall Ferguson, Ralf Stolz, Nick Talbot, Gang Lu, Timo Allison, Markus Brandl, Victor Gomez, Wei Cao, and Adrian Kipka are members of the Trimble Engineering Team in Höhenkirchen, German
Adding GLONASS to GPS gives a total of about 50 satellites, for a significant improvement in navigation availability, reliability, robustness, and convergence time through a new multi-GNSS precise point positioning (PPP) service. System performance and field results demonstrate that there is no need to await future constellations — better performance is available now.
By Tor Melgard, Erik Vigen, Ole Ørpen, Fugro Seastar AS, and Jon Helge Ulstein, Bourbon Offshore Norway AS
Precise point positioning (PPP) stands out as an optimal approach for providing global augmentation services using current and coming GNSS constellations. PPP requires fewer reference stations globally than classic differential approaches, one set of precise orbit and clock data is valid for all users everywhere, and the solution is largely unaffected by individual reference-station failures. There are always many reference stations observing the same satellite because the precise orbits and clocks are calculated from a global network of reference stations. As a result, PPP gives a highly redundant and robust position solution.
The results presented here represent a significant step forward in PPP GNSS research and development. Using GLONASS improves the availability and reliability of the solution. The G2 system’s horizontal positioning accuracy is at the decimeter level. These results derive from increasing the number of satellites in the constellation by 60 percent, from about 30 to 50 satellites. The outcome of the development of the G2 real-time combined GPS and GLONASS PPP service represents a next-generation GNSS augmentation. Further, the later GLONASS-M satellites have improved performance and lifetime over previous GLONASS satellites, so that results will continue to improve as that constellation is replenished.
G2 development has benefited from the close cooperation between Fugro and the European Space Operation Centre (ESOC), an establishment of the European Space Agency (ESA). ESOC has contributed its long experience and expertise on precise orbit and clock processing techniques, while the strength of Fugro is real-time positioning and navigation services.
Based on this work, Fugro has introduced the first real-time GPS and GLONASS precise orbit and clock service. The service utilizes Fugro’s own network of dual-system GNSS reference stations to calculate precise orbits and clocks on a satellite-by-satellite basis for all 50 satellites of the two global navigation satellite systems. The system comprises about 40 dual-frequency GPS and GLONASS reference stations distributed around the world as shown in Figure 1.
Raw GNSS measurement data for all satellites are transmitted to processing centers for calculation of the precise orbit and clock of each GPS and GLONASS satellite (Figure 3). The precise data generated is then broadcast to users via geostationary communications satellites with nearly global coverage, as shown in Figure 2.
FIGURE 1. The G2 reference station network consists currently of 40 GNSS receivers owned and operated by Fugro.FIGURE 2. The G2 precise orbits and clocks are broadcast over redundant geostationary satellite beams together with the other Fugro services.FIGURE 3. Dataflow from the reference stations to the redundant calculation servers producing precise orbits and clocks, then to the satellite uplink stations for broadcast over geostationary satellites to combined G2/GNSS user equipment.
Inside the end-user equipment a dual-frequency carrier-phase-based PPP solution gives horizontal positioning accuracy at the decimeter level. The PPP calculation module is provided by Fugro and is embedded in multiple GNSS receiver manufacturers’ products as well as Fugro’s own product line.
Like any GNSS technique, PPP is affected by satellite line-of-sight obstructions. Even the most precise orbit and clock data is useless if the user cannot track particular satellites. When satellite visibility is partially obstructed, a best possible service can be ensured by using the full range of satellites from both the GPS and GLONASS systems. This can occur during a survey of a dense urban environment, and for urban positioning in general. It can occur under heavy tree cover, when a cruise ship is in a high-sided fjord, when an offshore vessel is close to an oil rig or platform, or during ionospheric disturbances.
The trend clearly lies towards increasing availability of GNSS satellites on orbit; many studies predict the future benefits of combining the constellations of GPS and Galileo. There is no need, however, to wait for future constellations to reap the immediate benefits of access to additional GNSS satellites. The current GLONASS constellation may not have all the features of future GNSS systems, but it is available here and now. Recently, the Russian government has proven its commitment to enhancing the GLONASS constellation. Many receiver manufacturers have also acknowledged this fact and now provide combined GPS and GLONASS receivers.
G2 Accuracy and Statistics
In Figure 4, time-series plots show the 3D accuracy of GPS and GLONASS G2 real-time orbits on August 14, 2009. In the comparison, final orbit data from the International GNSS Service (IGS) is used as reference. PPP positioning is mainly affected by the radial orbit error, which is significantly less than the total 3D error shown here. The 95 percent 3D accuracy for GLONASS (22 centimeters) is more than double that for GPS (10 centimeters). The graph demonstrates how this difference in this case is mainly caused by a few GLONASS satellites being less accurate. Actually, several GLONASS satellites have orbit accuracy very close to the level of GPS for real-time G2 data.
FIGURE 4. GPS and GLONASS orbits compared to IGS final orbits.
Figure 5 shows the clock accuracy of the G2 real-time clocks compared to final IGS clocks. A constant bias has been removed to account for the differences in system reference time. Smaller individual clock biases for each satellite can still be observed. Small biases do not affect the final accuracy of the PPP solution, and achievable position accuracy with these clocks are significantly better than the 21-centimeter 95 percent number for GPS may indicate.
FIGURE 5. GPS clocks compared to IGS final clocks. GLONASS clocks compared to a combined solution based on IGS plus Fugro network to calculate a best possible reference solution.
The lower time series in Figure 5 shows the estimated GLONASS clock accuracy. Currently there is no comparable IGS product with precise GLONASS clocks. A post-processing of all available IGS plus Fugro GNSS stations has been made to establish a reference for the comparison. As shown, the GLONASS clocks are more variable, but still they are stable enough to allow for precise navigation.
Real-Time Positioning Results
Real-time position performance is continuously observed at the G2 operation and monitoring center in Oslo, Norway. The graph in Figure 6 shows typical G2 positioning results with the calculation engine running in dynamic mode at a fixed location for a 24-hour period. The blue lines in the north and east time series are at 20 centimeters and the scale is 61 meter. In the height graph the blue lines indicate the 30-centimeter level. The antenna is in a location with clear view of the sky, and in
dependently calculated reference coordinates are used as reference. 1-sigma accuracy statistics on August 14 are 3, 4, and 8 centimeters in easting, northing and height respectively.
FIGURE 6. G2 GPS-plus-GLONASS position monitoring results in Oslo on August 14, 2009.
Figure 7 shows GLONASS-only real-time positioning with clear view of the sky for the same day as in Figure 6 and the same antenna location. The blue line indicates the 50-centimeter level and the scale is 62 meters. For long periods, the GLONASS-only solution works quite nicely. There are, however, shorter periods with fewer than four satellites being tracked, causing the position output to stop, followed by a period of re-convergence.
FIGURE 7. GLONASS-only real-time PPP solution on August 14, 2009 for a 24-hour period.
Figure 8 displays results from May 11, 2009, when there were slightly more satellites available and just enough to have the GLONASS-only solution running for 24 hours without resets. 1-sigma accuracy statistics for this day are 11, 9, and 16 centimeters in easting, northing, and height respectively. Considering the average number of satellites of 6.14 and periods with high DOP values, this is very promising. In early 2010, 20 GLONASS satellites should be available, and by 2011, 24 are expected. In 2010, a performance similar to or better than that of May 11 should generally be expected with the new satellites. By 2011, even better performance is believed to become the norm of GLONASS-only real-time PPP navigation.
FIGURE 8. GLONASS-only real-time PPP solution on May 11 for a 24-hour period.
Even in some clear-view-of-sky situations, the addition of GLONASS may improve the navigation compared to GPS-only solutions. Figure 9 presents an example of such situations. Here the GPS-only solution suffers some multipath-like effects showing up, especially in the east component. Figure 10 shows the combined GPS+GLONASS solution for the same dataset. The distortion in position is practically eliminated. This is an example where adding GLONASS also improves redundancy and accuracy for navigation with clear view of the sky.
FIGURE 9. GPS-only results for a 3-hour period where some multipath-like effects distort the postition, especially the east component.FIGURE 10. Adding GLONASS improves redundancy and accuracy for the same time period as presented in Figure 9.
The next test further analyzes the same dataset as in Figures 9 and 10 by simulating a virtual wall to the south, blocking all satellites below 40 degrees elevation. Figure 11 illustrates this virtual wall blocking both GPS and GLONASS satellites.
FIGURE 11. GPS and GLONASS satellites blocked between the azimuths 90 and 270 degrees and elevation lower than 40 degrees, effectively establishing virtual wall to the south.
With such data blockage, the GPS-only solution fails for more than 20 minutes, as seen in Figure 12, simply because the number of satellites goes below four. Then a period with slow convergence follows because of few satellites and high DOP.
FIGURE 12. GPS-only solution fails when simulating blockage to the south.
Again, adding GLONASS greatly improves the performance, as shown in Figure 13. Now a sufficient number of satellites are tracked all the time, and there is a continuous solution with the combined GPS+GLONASS throughout the time window when the GPS-only solution failed.
FIGURE 13. GPS+GLONASS solution continues working with simulated blockage to the south.
Even with more than 30 satellites in the GPS constellation, there are situations when the satellite geometry gets poor. This occurred in northwest Europe on February 2, 2010. One of the GPS satellites (PRN17) was not available due to maintenance, and even with five to six usable GPS satellites left, the horizontal dilution of precision (HDOP) was in the range of 7–11 for about 12 minutes (10-degree elevation mask), as shown in figure 14. Such high HDOP values lie above what most user installations are configured to accept, and Fugro received feedback from clients at sea losing positioning. The G2 solution was not affected by the poor GPS geometry and kept the HDOP below 2 during this period, as shown in Figure 15.
FIGURE 14. GPS-only performance during a period with poor GPS satellite geometry in Oslo, February 2, 2010.FIGURE 15. GPS+GLONASS performance during the same period as in Figure 14 in Oslo, February 2, 2010.
Convergence-Time Analysis
As will be shown in the following analysis, adding GLONASS not only improves availability and robustness of the solution, it greatly improves convergence time. Real-time high-accuracy PPP solutions use carrier-phase measurements to achieve high-accuracy positioning. To do so, the carrier-phase ambiguities must be determined. This process takes a certain time depending on the observed satellite geometry and is commonly referred to as cold-start convergence time.
Figure 16 presents a theoretical study of the expected convergence time for a GPS-only compared to a combined GPS+GLONASS solution. The lower graph shows how the expected convergence time varies significantly for a GPS-only solution throughout the day, with a peak of 75 minutes. The combined solution shows much more consistent performance, with expected 50–60 percent average improvement over GPS-only.
FIGURE 16. Theoretical study of expected convergence time with actual GPS-and-GLONASS constellation in view of Oslo on June 26, 2009. Adding GLONASS gives a 50–60 percent theoretical convergence time improvement over GPS-only.
We compare this theoretical study to results using G2 data produced in real time in Figure 17. A cold start is performed every 5 minutes throughout the day, for six consecutive days, giving a total of 1,728 convergence tests. The convergence criterion is the time when the 3D position arrives within 40 centimeters of the reference position and remains there for a minimum of 10 minutes. The average convergence time improvement achieved in Figure 17 is 39 percent, with some variations from day to day. On the better days, the average improvement is almost 50 percent, and close to the expected performance based on the theoretical study. On other days, there is room for further improvement. Mainly two factors are expected to contribute: more and newer GLONASS satellites, and further improvements of the G2 precise GPS and GLONASS orbit and clock product.
FIGURE 17. Convergence results for six consecutive days starting June 24, 2009. Average convergence time of GPS-only is 27 minutes, and GPS+GLONASS is 16.5 minutes, a 39 percent improvement.
Dynamic Environment Results
Since late 2008, the G2 system has been installed on the vessel Bourbon Topaz, making frequent trips into the North Sea and back into port in Norway (see BOX).
All positioning data from both the G2 system and the GPS-only reference systems are logged in real time on the vessel. Figure 18 gives an example plot of the relative height estimated by the G2 GPS-GLONASS solution. In the beginning of the plot, the vessel is out at sea, clearly seen as a noise in the graph that actually is the vessel’s movement in the waves. Then the vessel comes into port and the slower tidal variations are observed for the next 12 hours until the vessel again goes back out to sea.
FIGURE 18. Relative G2 height measurements for a 24 hour period. The vessel is in harbor from 04:00 – 16:00 UTC.
On June 22, 2009, an incident was recorded where the combined GPS-GLONASS G2 solution improves performance. As seen in Figure 19, there is a period starting at 10:00 UTC where the GPS-only reference systems suffer from poorer DOP values, and this is reflected both in horizontal and vertical components of the calculated position. This particular plot shows how the height drifts off by roughly 1 meter while the G2 combined solution remains unaffected for the entire period. Generally, the G2 solution also shows a smoother height than the reference system even when such problems as shown here are not present.
FIGURE 19. Height graph from the Bourbon Topaz while in harbor on June 22, 2009. The GPS-only reference system has a period with poor DOP values while the GPS-plus-GLONASS solution is not affected.
The Bourbon Topaz carries the G2 system on operations in the North Sea, and continuously compares it with the GPS-only reference systems onboard.
Test of G2 onboard Bourbon Topaz
The Bourbon Topaz is a modern supply vessel equipped with the latest dynamic positioning (DP) systems, operating in the North Sea. The North Sea can be a harsh environment in which to operate, and we rely on good tools for maneuvering our vessels.
Early on, we recognized the need for stable, reliable reference systems, and our fleet is equipped with Kongsberg Seatex DPS700 system as standard. When we were asked to test the G2 onboard the Bourbon Topaz, we saw this as an opportunity to follow the development in the industry of such services. The DPS232 receiver was set up in connection with the vessel’s DPS700 system, and all information was logged and sent to Fugro Seastar.
We often experience that the vessel has to operate close to offshore installations, which could block good reception of signals. In these cases, the G2 offers a much better and more reliable signal reception. Our experience of the quality of the G2 system is overall positive.
User Equipment
G2 and the other Fugro services can be received from a variety of different user equipment; both Fugro-branded or manufactured equipment and third-party equipment. In most cases the L-band receiver decoding the data from the geostationary satellites, including Fugro subscription software and position calculation module, is integrated into the same box as the GNSS receiver. Both the GNSS and geostationary satellite signals can be tracked with a single antenna.
FIGURE 20. Receivers supporting the Fugro services. These are only examples, and not all third-party equipment manufacturers are shown. Fugro L-band data reception receiver and positioning/subscription software reside inside the receiver.
Conclusions
Test results confirm decimeter-level position accuracy in real-time navigation with G2, the first real-time combined GPS and GLONASS PPP service. Several examples show how G2 improves availability, robustness, and convergence time compared to GPS-only positioning.
More is better. There is no need to wait for future constellations like Galileo to reap the benefits of access to additional GNSS satellites now.
Tor Melgard is R&D manager at Fugro Seastar in Oslo, Norway. He holds an M.Sc. in electrical engineering from the Norwegian Institute of Technology and wrote his thesis at the Department of Geomatics Engineering, University of Calgary.
Erik Vigen is a senior developer at Fugro Seastar. He received his M.Sc. in Geodesy from the Norwegian Institute of Technology.
Ole Ørpen is senior scientist at Fugro Seastar. He received his M.Sc. from the Norwegian Institute of Technology in electrical engineering.
Jon Helge Ulstein is IT superintendent at Bourbon Offshore Norway AS, a subsidiary of the Bourbon Group, Marseilles, France.
