Galileo’s Surveying Potential: E5 Pseudorange Precision

March 1, 2012  - By 0 Comments

By Ismael Colomina, Christian Miranda, M. Eulàlia Parés, Marcus Andreotti, Chris Hill, Pedro F. da Silva, João S. Silva, Tiago Peres, João F. Galera Monico, Paulo O. Camargo, Antonio Fernández, José Maria Palomo, João Moreira, Gustavo Streiff, Emerson Z. Granemann, and Carmen Aguilera

New Galileo signals have great potential for pseudorange-based surveying and mapping in both optimal open-sky conditions and suboptimal under-canopy environments. This article reviews the main features of Galileo’s E5 AltBOC and E1 CBOC signals, describes generation of realistic E5 and E1 pseudoranges with and without multipath sources, and presents anticipated horizontal positioning accuracy results, ranging from 4 centimeters (open-sky) to 14 centimeters (under-canopy) for E5/E1.

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The history of GNSS surveying has been written in the carrier phase language — until now. The well known reason for this is the high precision, at the millimeter level, of the carrier phase observables and the low precision, at half a meter or worse, of the pseudorange observables. The progress and results of carrier-phase positioning are also well known and, today, surveyors can count on many effective ways for relative and absolute, static and kinematic, accurate positioning procedures like RTK, PPP and others. On the other hand, pseudorange observables have been used for various cadastral, GIS and mapping applications with meter and lower level accuracy requirements. The main advantages of pseudorange positioning are the simplicity and robustness of data processing. Moreover, the typical user of GNSS (pseudorange) mapping gear needs less GNSS education and training than the typical GNSS geodetic surveyor.

However, there are cadastral and mapping applications that require better accuracies than current pseudoranges provide and there are surveying applications that do not require the cm to dm level accuracies that carrier phases provide. There is a gap where no choice is optimal: either the choice is unnecessarily expensive (receivers, processing software, trained personnel) or it is unacceptably inaccurate. This gap can be reduced or eliminated with the new GPS and Galileo signals. It is therefore convenient that the size of the new smaller gap, if any, be analyzed as soon as possible even if the analysis has to rely on simulated signals.

According to the simulations performed, it is expected that pseudoranges can be extracted from the Galileo E5 AltBOC signals with tracking errors (1-σ level) ranging from 0.02 m (“open sky” scenarios) to 0.08 m (“tree covered” scenarios with 15% through-foliage visibility) whereas for the Galileo E1 CBOC signals the tracking errors range between 0.25 m and 2.00 m respectively. With these tracking errors and with the explicit estimation of the ionosphere parameters, the available simulations indicate “open sky” horizontal/vertical accuracies of 0.04/0.17 m for static positioning and 0.04/0.20 m ones for (low dynamics) kinematic positioning; and “tree covered” accuracies of 0.05-0.13/0.07-0.30 m for static positioning and 0.15/0.35 m for (low dynamics) kinematic positioning.

The high precision of the Galileo E5 AltBOC range measurements suggests that their modeling can benefit from available research results of the precise point positioning (PPP) carrier phase-based techniques. Since, in contrast to carrier phase measurements, pseudoranges are not ambiguous, it is expected that the convergence challenges of PPP will disappear or largely be mitigated when using cm-level precise pseudoranges. As a result, in addition to standard relative positioning surveying, absolute positioning surveying is likely to emerge as a standard procedure, both in real-time (using Galileo ultra-rapid orbits hopefully available in future from the IGS) or in post-processing (similarly, using IGS final precise Galileo orbits). Clearly, the question is how fast and how well the unknown parameters in the pseudorange model will converge to the correct values. However, even low convergence might be a minor problem as, with pseudoranges, loss-of-lock situations do not require the re-initialization of some parameters in the estimation algorithms.

Absolute pseudorange positioning is of particular interest because simple GNSS surveying with pseudoranges can become a practical tool in regions with sparse GNSS permanent station distributions and for communities with limited surveying expertise. As the results and behavior of E5 AltBOC pseudorange positioning consolidate and become well understood, appropriate surveying procedures will be identified and adopted. The starting point for this is the investigation of static (absolute) and kinematic (with known initial/end points) positioning with E5 AltBOC and E1 CBOC.

The full deployment of the Galileo constellation — Full Operational capability (FOC) — is currently scheduled for 2020. As of now, two satellites of the In-Orbit Validation (IOV) have been launched and two more will follow that will complement the two experimental satellites (GIOVE-A and GIOVE-B) already in orbit. The Initial Operational Capability (IOC) is scheduled for 2014 and will include fourteen satellites that were ordered in January 2010. In addition to this, eight additional satellites have been ordered in February 2012.

Although not covered in this paper, we note that there are a number of potential ways to benefit from the E5 AltBOC signal and modulation before Galileo FOC. One of them is to combine the E1/E5 Galileo signals with the L1/L5 GPS signals and “replace” the missing Galileo signals with GPS ones. Another one that will depend on the IOV satellite configuration is to keep on working with full GPS L1/L2 satellite constellations and “assist” GPS with Galileo to speed up convergence periods in PPP or to extend the ranges of Differential GPS (DGPS).

In the paper we concentrate on the combination of E1 CBOC and E5 AltBOC signals and modulations by explicitly estimating the ionospheric bias — or a correction with respect to a model — instead of forming ionospheric-free combinations. The reason for this is that, since the E1 CBOC and E5 AltBOC pseudoranges have disparate noise levels, in the resulting ionospheric-free pseudoranges the low noise properties of E5 AltBOC will be lost. (We note the alternative method, in the presence of precise pseudoranges, of taking advantage of the ionospheric divergence of carrier phase and pseudoranges. In this approach I sr or δI sr are estimated with the use of just the E5 frequency.)

The research reported in this paper has been conducted in the frame of the international –EU and Brazil – ENCORE project. ENCORE –Enhanced Code Galileo Receiver for Land Management in Brazil – is funded by the European Commission (grant 247939) with the aim to implement the 7th European Framework Program for Research and Development (FP7). The project runs from 2010 to 2012 and is realized by a European-Brazilian consortium lead by DEIMOS Engenharia (Portugal). The goals of ENCORE are the introduction of Galileo terminals in the Brazilian market for land management applications, the stimulation of the participation of Brazilian entities in Galileo and the development of a high-precision and low-cost land management application based on Galileo signals.

The Galileo Signals

The development of new GNSS systems, as the Galileo system (as well as the modernization of currently available ones, as the GPS) will provide additional signals with increasingly complex modulations and multiplexing schemes, enabling performance enhancements in terms of availability, accuracy, and robustness.

Tracking accuracy and multipath robustness are closely related to the slope of the (main) peak of the Auto-Correlation Function (ACF) of the signals. Figure 1 shows the ACFs for the most relevant GPS and Galileo modulations. Figure 2 shows the multipath error envelopes for the corresponding GPS and Galileo signals when using an Early-Late Power discriminator and a correlator spacing of 0.1 chip (assuming one reflected ray and a carrier over multipath ratio of 2).

 Figure 1. Normalized auto-correlation functions for different modulations: BPSK (n) of GPS L1, BOC (n,n) of Galileo E1 with simplified demodulation, CBOC (6n,n,1/11) of Galileo E1, and AltBOC (1.5n,n) of Galileo E5 signals.

Figure 1. Normalized auto-correlation functions for different modulations: BPSK (n) of GPS L1, BOC (n,n) of Galileo E1 with simplified demodulation, CBOC (6n,n,1/11) of Galileo E1, and AltBOC (1.5n,n) of Galileo E5 signals.

Multiplexed BOC (MBOC) is a new modulation introduced in 2006, and included recently in the Galileo SIS ICD. The E1 Open Service modulation receives the name of Composite Binary Offset Carrier (CBOC) and is a particular implementation of MBOC. The CBOC (6,1,1/11) modulation is the result of a linear combination of a wideband BOC (6,1) sub-carrier with a narrow-band BOC (1,1) sub-carrier, in such a way that 1/11 of the power is allocated (in average) to the high frequency component.

The Galileo CBOC (6,1,1/11) signal’s demodulation can be simplified by using a BOC (1,1) modulated local replica, at the expense of tracking and multipath robustness performance (making it comparable to that of a BOC (1,1) signal) but enabling an interesting trade-off between performance and receiver complexity. In the current work the CBOC modulation is assumed.

Nevertheless, the potential of the future Galileo E5 signal is expected to outshine even these modernized signals. The Galileo E5 signal, with its Alternative Binary Offset Carrier (AltBOC) modulation, is one of the most advanced and promising signals of the Galileo system. Receivers capable of tracking this signal will benefit from unequalled performance in terms of measurement accuracy, precision, and multipath suppression. However, the signal processing techniques to implement a matched-filter AltBOC demodulation are much more challenging than those for the traditional BPSK or even for the BOC modulations (as the current GPS L1 C/A or future L1 C signals). This stems from the large bandwidth (chip rate), complex sub-carrier, elaborate multiplexing scheme (which enables the simultaneous broadcast of 4 channels on a single carrier) and complex interaction of the 4 multiplexed channels.

The AltBOC (15,10) correlation peak is similar to the one of BOC(15,10) near the main peak and, as suggested in Figures 1 and 2, it outperforms all other modulations of the current and future GPS and Galileo civil and open service signals (note that the x axis of Figure 1 is also normalized by the chip period, which is 10 times shorter for the AltBOC (15,10) modulation than for the remaining ones).

 Figure 2. Multipath error envelopes for GPS L1 (BPSK(1)), Galileo E1 (demodulated as BOC (1,1) and CBOC (6,1,1/11)), and Galileo E5 AltBOC (15,10) signals (Early-Late Power discriminator, correlator spacing of 0.1 chip, carrier over multipath ratio of 2 and infinite bandwidth).

Figure 2. Multipath error envelopes for GPS L1 (BPSK(1)), Galileo E1 (demodulated as BOC (1,1) and CBOC (6,1,1/11)), and Galileo E5 AltBOC (15,10) signals (Early-Late Power discriminator, correlator spacing of 0.1 chip, carrier over multipath ratio of 2 and infinite bandwidth).

The E5 signal can be separated into two sub-bands (E5a and E5b) which can be treated separately by a Galileo E5 receiver (as BPSK (10) modulated signals), called Single Side-Band (SSB) processing. However, this would result in the loss of the promising AltBOC signal properties (resulting in a classical triangular ACF). Hence, a matched filter demodulation of the full Galileo E5 signal is desired to implement the best possible receiver in terms of accuracy and multipath robustness, at the expense of an increase in the receiver complexity and required bandwidth.

The existence of secondary peaks (as shown in Figure 1) in the ACFs of Binary Offset Carrier (BOC) modulations (as the AltBOC and CBOC) require specific techniques (i.e., bump-jumping) to ensure that the main peak is the one being tracked.

According to the simulations performed, in the absence of multipath or signal fading sources the performances achievable with E5 AltBOC and E1 CBOC in terms of accuracy of the code tracking errors is 0.02 m and 0.25 m respectively at 45 degree (about 40 dB-Hz for E1 and 44 dB-Hz for E5) with a correlator spacing of 0.1 chip and integration times of 4 ms.

If multipath and signal fading sources are present, the expected errors increase to 0.08 m and 2 m respectively (for about 36 dB-Hz for E1 and 40 dB-Hz for E5). Longer integration times will lead to better performances.

During the project, the above simulation results will be compared against those obtained with Galileo live signals. Figure 3 shows the ENCORE hardware receiver prototype, which is composed by the FPGA board, the RF FE board, the LNA and the antenna. The mezzanine board and the two voltage converters, which can also be seen in figure, enable the receiver testing using recorded IF signals or synthetic IF data.

 Figure 3. ENCORE hardware receiver prototype.

Figure 3. ENCORE hardware receiver prototype.

Positioning Models and Algorithms

The observation equations for pseudorange measurements follow the modelling principles of PPP. Thus, the observed pseudoranges P1sr (E1 CBOC) and P5sr (E5 AltBOC) can be modeled as

Screen shot 2013-01-04 at 7.17.32 PM (1)

for i = 1,5, where ρsr is the true geometric distance between satellite s and receiver r, c is the speed of light in a vacuum, δts is the given s satellite clock correction, R s is the relativistic “correction” for satellite s, T sr is the modelled or given tropospheric delay, f1, f5 are the frequencies of E1 CBOC and E5 AltBOC respectively, I sr / f 2i are the modelled or given ionospheric delays, and bis are the given biases for satellite s.

In the above pseudorange observation equation, we will estimate the receiver position Xr (included in ρ sr ), the receiver clock correction δtr , the correction δT sr to the modelled or given tropospheric delay T sr , the term δI sr related to the correction δI sr / f 2i to the modelled or given ionospheric delays I sr / f 2i , and the receiver frequency dependent biases bir. In equation 1, ρsr is a well-known function of the satellite ephemeris, the receiver position, the satellite and receiver antenna phase centre offsets, and of all the effects, like solid Earth tides, usually included in PPP models.

The time dependent unknown parameters in equation 1 are further modelled as random walk stochastic processes for the stochastic differential equation of the prediction step (Kalman filter estimation approach) or of the dynamic model (dynamic network estimation approach) as follows: δtr is a random walk with rather large driving white noise variance [rw (∞)]; δT sr as rw (0.0152 m2), PSD level; bir as rw (0.00172 m2), PSD level (b1r is set to 0); and (I sr + δI sr ) / f 2i as rw (σ2 m 2 ) with

Screen shot 2013-01-04 at 7.17.45 PM(2)

where Screen shot 2013-01-04 at 7.25.01 PM, T = 64 × 60 s, and τ is the time interval (in seconds) between two successive measurements. Clearly, the stochastic model for the total ionospheric delay depends on assumptions for Screen shot 2013-01-04 at 7.25.59 PMand T that also depend on the solar activity. Furthermore, depending on the model or data used for I sr the actual parameter to be estimated δI sr and, specifically δI sr , / f 2i will obey to different “amplitude” and “time correlation” T values. For the results reported in the paper, the three-dimensional, time dependent ionospheric electron density NeQuick model was used for I sr . For δI sr , / f 2i , the values Screen shot 2013-01-04 at 7.26.54 PM, T = 5 × 60 s, were adopted.

In the ENCORE project, the above models are being used to investigate the performance of the various positioning modes (absolute and relative, static and kinematic) and procedures (with and without a “ground presurveyed” or “ground control” point in the absolute positioning mode).

Simulation Scenarios

Due to the unavailability of sufficient Galileo space vehicles at the moment, the validation of the algorithms described before was done using the Navigation Sensor Simulation (NSS) tool, developed by University of Nottingham. The NSS data simulation tool was originally designed to simulate the types of measurements that can be made using a GNSS receiver. Specifically the simulator has the capability of producing code, carrier and Doppler measurements on L1, E1, E5a, E5b, E5 (combined), L2c, L5, and E6 frequencies, covering GPS and Galileo systems. The simulation is achieved by using the true locations of both the receiver and the satellites to calculate the true, error-free measurements. Error models are then applied to account for the various inaccuracies seen in real-world measurements. The simulation results are returned to the user in a file in the standard Receiver Independent Exchange (RINEX) observations format.

The user of the NSS tool is required to define a simulation scenario. The main inputs from a scenario definition are the satellite ephemeris data and the true location of the receiver as well as the parameters for the various error models and the time period for which data should be simulated. It is possible to simulate data using the true locations of the satellites for any day in the past.

For the purpose of this work, the precise orbits used for the Galileo system were obtained from the GalileoSat System Simulation Facility (GSSF) simulator. The expected error on the estimated values for BGD (E1 E5a) and BGD (E1 E5b) was also applied,

NSS provides models for the two types of discriminator widely used in GPS receivers: the Early-Minus-Late Power (EMLP) and the Dot-Product (DP) discriminators. For this, NSS accepts parameters for front-end filter bandwidth, correlator spacing, DLL loop bandwidth and integration time for each of the signal modulations it is capable to work with: GPS BPSK (1), GPS BPSK (10), Galileo CBOC (6, 1, 1/11), and Galileo AltBOC (15, 10).

Table1

Table 1. Galileo orbit error factors applied.

 Table 2. Parameters for the generation of the simulated pseudoranges.

Table 2. Parameters for the generation of the simulated pseudoranges.

C/No values for GPS and Galileo for various satellite elevation angles are tabled inside NSS in accordance with measurements available from various sources. The values in those tables are interpolated via respective spline equations for intermediate elevation angles.

For the scope of the ENCORE project and its application for land management in rural areas, it is assumed that the influence of the vegetation on the satellite signals will be of creating diffuse, non-coherent signal scattering, resulting in signal loss but not significantly in signal delay. Therefore the ITU-R model is of greater interest as this model gives empirical values of cumulative signal fade due to tree shadowing, based in multiple measurement campaigns. The ITU-R signal fading model takes as input the signal frequency, the satellite elevation angle and the “estimated signal visibility percentage” of the signal. This last parameter accounts for the foliage effect on the signal, and will have a low value when the tree is in full foliage and a high value when the trees are without leaves.

For the tropospheric delay, NSS makes use of the EGNOS Troposphere Model, although in NSS this model is used to simulate the delay experienced due to the troposphere rather than correct for it. For the ionospheric delay, NSS has been developed to read Total Electron Content (TEC) maps in the standard IONEX file format. These files may contain 2 or 3 dimensional maps of the TEC at a number of equally spaced epochs, usually covering a 24 hour period. The TEC for each sub-ionospheric pierce point at a given epoch is calculated by interpolating between two TEC maps at consecutive epochs. The maps are firstly rotated around the z-axis to compensate for the strong correlation between the ionosphere and the sun’s position. A standard 4 point interpolation scheme is then used to interpolate each TEC map to the required latitude and longitude.

The scenario definition is completed by selecting the number and type of measurements to be simulated along with the data interval for the measurements and the elevation masking angle of the receiver.

The preliminary results presented in this paper are based on simulation scenarios created from the base settings presented in tables 1 and 2, for the “open sky” (OS) and “tree covered” (TC) cases, using 8 Galileo satellites (of a 27-satellite constellation) for a fixed point in Brazil that has been processed in the absolute and static/kinematic modes. Thus 10 cases have been investigated that result from combining the OS and TC ones with the kinematic (K) and static (S) cases. The static cases have been computed for observation periods of 1, 5, 10 and 30 minutes respectively (cases S-1, S-5, S-10 and S-30). For all test cases a 45 minute data set measured at 1 Hz has been processed together with start/end initialization periods –i.e., observations processed in the static mode– of 5/10 minutes respectively. Thus, the test OS S-5 (confer table 3) corresponds to the “open sky” scenario for static point determination with observation periods of 5 minutes and the test TC-K corresponds to the “tree covered” scenario for kinematic point determination at 1 Hz.

Results from Simulated Measurements

Table 3 summarizes the results of the tests described in the previous section. Each table cell contains the Root Mean Square Error (RMSE) of the horizontal (μH) and vertical (μV) positioning results when compared to the known true value of the fixed point established for the simulations. Figures 4 to 7 represent the receiver’s position and clock errors for the OS and TC cases. Note again, that positioning is performed in the absolute and post-processing mode.

Col-4

Figure 4. Position accuracy for the Open Sky scenario, case K.

 Figure 5. Receiver’s clock accuracy for the Open Sky scenario, case K.

Figure 5. Receiver’s clock accuracy for the Open Sky scenario, case K.

 Figure 6. Position accuracy for the Tree Covered scenario, case K.

Figure 6. Position accuracy for the Tree Covered scenario, case K.

 Figure 7. Receiver’s clock accuracy for the Tree Covered scenario, case K.

Figure 7. Receiver’s clock accuracy for the Tree Covered scenario, case K.

Although the results can still be considered preliminary, they illustrate what can be expected from the proposed combination of E1 and E5 Galileo pseudoranges. The horizontal accuracy estimator μH is computed as μH=√ μ2E + μ2N where μE , μN are the position RMSE in the North and East components respectively; μV is the position RMSE in the height component. In the OS scenario, the horizontal accuracy estimator is always below 10 centimeters and is rather independent of the processing mode as the horizontal accuracy of kinematic positioning (μH = 7 centimeters) does not differ much from that of half-an-hour positioning (μH = 5 centimeters). When, in the future, actual Galileo E1 and E5 measurements can be used instead of simulated ones, it is likely that remaining unmodelled systematic errors slightly worsen the reported positioning accuracy. As usual, this can be overcome with differential positioning at the expense of loosing some precision. On the other side, an easy and robust procedure for absolute positioning is of interest for land surveying and cadastral mapping of vast areas. The mentioned values, even if they may seem optimistic because of their simulated origin, still fall comfortably within the specifications of the official Brazilian National Institute for Colonization and Agrarian Reform (INCRA) for all surveying categories down to the fundamental C1 ( μH = 10 cm). In Figure 4, the results of the kinematic positioning simulation exhibit a remaining systematic, rather constant and at the few cm level, error dominating the N and E horizontal components. The vertical error is much noisier than the horizontal one and this behaviour may indicate that further research on the overall modelling of the combined E5/E1 signals is required. However, model fine tuning in the absence of actual signals has its limitations and dangers and, therefore, no big effort has been devoted to this issue. Last but not least, vertical accuracy ranges between μV = 19 centimeters for kinematic positioning and μV = 12 centimeters, for the kinematic and half-an-hour static cases respectively. The same discussion applies here as for the horizontal case, when the actual Galileo signals become available.

Table 3 also contains the corresponding RMSE results for the TC case. As expected they are worse than those of the OS case and range between μH = 14 cm (kinematic case) to μH = 7 cm (half-an-hour static case). In all cases, they would meet the C2 INCRA category (μH = 20 cm). Vertical accuracy ranges from μV = 35 cm (kinematic case) to μV = 18 cm (static case, S-10) to μV = 0.07 (static case, S-30) although the last S-30 result is thought to be a lucky coincidence rather than a representative figure.

Table3

Table 3. Empirical results (errors) of point positioning for the E1/E5 combination (click to enlarge).

Conclusions and Ongoing Work

We have discussed the potential of the combination of Galileo E1 CBOC and E5 AltBOC pseudoranges for surveying and mapping applications in the frame of the international cooperation Galileo project ENCORE. Via simulations, we have investigated the tracking precision of the E1 and E5 pseudoranges under “open sky” and strong “tree coverage” scenarios resulting in 0.25 to 2.00 m (E1) and 0.02 to 0.08 m (E5) pseudorange precisions. We have further investigated the post-processed results — therefore with final precise Galileo orbits — in the OS and TC scenarios cases for kinematic and static modes and given preliminary results.

According to them, in the OS case, the positioning accuracy of the used E1/E5 combination and parameter estimation approach is at the cm-level for the E, N horizontal components and at the dm level for the height component. In the TC case, the accuracy estimates are at the low dm-level for the horizontal components and at the dm-level for the vertical ones. In the OS case, the INCRA C1 tolerances are met and in the TC case, the C2 tolerances are met. The accuracy estimates are at the low dm-level for the horizontal components and at the dm-level for the vertical one.

In the next months, up to the completion of the ENCORE project, we plan on extending the simulation analysis to the whole scenario spectra, with and without a complete Galileo constellation, with and without GPS L1/L5 measurements, in static and kinematic modes, in real-time and post-processing modes, and with precision and broadcast orbits. In parallel, we also plan to finish the E5/E1 ENCORE prototype receiver and software, a joint effort of DEIMOS Engenharia and OrbiSat da Amazônia, a Brazilian consortium member.

Acknowledgments

The reported research has been conducted within the “Enhanced Code Galileo Receiver for Land Management in Brazil” (ENCORE) project funded by the European Commission (grant 247939) with the aim to implement the 7th European Framework Program for Research and Development (FP7). The project runs from 2010 to 2012 and is realized by a European-Brazilian consortium lead by DEIMOS Engenharia (Portugal) and with participation of DEIMOS Space (Spain), the Institute of Geomatics (Spain), the Institute of Engineering Surveying and Space Geodesy of the University of Nottingham (UK), the São Paulo State University (UNESP, Brazil), OrbiSat da Amazônia (Brazil), Santiago e Cintra (Brazil) and MundoGeo (Brazil).


Ismael Colomina is director of the Institute of Geomatics (IG) of Spain, holds a Ph.D. in mathematics from the University of Barcelona (UB), and is a member of GPS World’s Editorial Advisory Board.

Christian Miranda received his MSc in telecommunication engineering and management from Universitat Politècnica de Catalunya. He is a research assistant at the IG.

M. Eulàlia Parés holds an MSc in meteorology and vlimatology (UB) and an MSc in airborne photogrammetry and remote sensing (IG). She is a research assistant and PhD candidate at the IG.

Marcus Andreotti received a Ph.D. in engineering surveying from the University of Nottingham (UN), where he was a research associate at the Institute of Engineering Surveying and Space Geodesy (IESSG). He is currently with NovAtel, Canada.

Chris Hill is a principal research fficer at the IESSG, holding a Ph.D. in satellite laser ranging.

Pedro F. Silva received his aerospace engineering degree from Instituto Superior Técnico (IST), Portugal. He works at DEIMOS Engenharia as head of the GNSS Division.

João S. Silva received his aerospace engineering degree from IST. He is currently a project manager in DEIMOS Engenharia’s GNSS Technologies Division.

Tiago Peres received his MSc degree in Aerospace Engineering from Instituto Superior Técnico, Portugal. He is a Project Engineer in the GNSS Technologies Division of DEIMOS Engenharia

João F. Galera Monico is an associate professor at the Universidade Estadual Paulista (UNESP), Brazil. He is a researcher and consultant of the Brazilian Research Council (CNPq), FAPESP and CAPES.

Paulo O. Camargo is an assistant doctor at UNESP, developing his post-doctoral activities at the National University of La Plata, Argentina.

Antonio Fernandez received an MSc degree in aeronautical engineering from the Polytechnical University of Madrid (UPM) and an MSc in physics from the UNED University of Spain. He is head of GNSS Division in the Aerospace Engineering Business Unit at DEIMOS Space, Spain.

José M. Palomo received a telecommunication engineering degree from the UPM. He works in GNSS receiver technologies and OFDM (WiMax) communication systems at DEIMOS Space.

João Moreira is technical director of Orbisat da Amazônia Indústria e Aerolevantamento SA. He received his Ph.D. in microwave technology at at theTechnical University of Munich.

Emerson Z. Granemann graduated in cartographic engineering from the Universidade Federal do Paraná, Brazil. He is founder and chief executive of MundoGEO Publishing.

Carmen Aguilera is market development officer at the European GNSS Agency. She holds an MSc in telecommunications engineering.

 

 

 

 

 

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