JAVAD GNSS has published a chart showing that it has tracked the IRNSS (Indian Regional Navigational Satellite System) L5 signal.
Shortly after the Indian Space Research Organization (ISRO) released its IRNSS Signal in Space Interface Control Document (ICD), JAVAD GNSS was able to track the L5 BPSK signal from both 1A and 1B satellites. Ability to track IRNSS L5 will be added to all JAVAD L5-capable receivers in the near future, the company said.
Rockwell Collins has successfully received and tracked a Galileo satellite signal using a prototype GNSS receiver designed for secure military use.
In 2013, Rockwell Collins received a $2 million contract from the Air Force Research Laboratory (AFRL) and the GPS Directorate to develop and demonstrate a Secure Software Defined Radio (S-SDR) GNSS receiver capability. By using multiple available satellite signals, improved and more robust signal availability can be obtained, enabling a compatible GNSS receiver to deliver superior position determination that can improve navigation performance and signal availability.
Hosted in a software-defined radio, the S-SDR program will develop the security architecture required for receiver equipment approvals and certifications. The arrival of modernized GPS signals and other global constellations is changing the way the U.S. military and its allies accomplish secure GNSS-based positioning, navigation and timing. The European Galileo constellation coming on line during 2015, including its open signals and secure Public Regulated Service, is expected to provide an opportunity for improved robustness in satellite based navigation, in both commercial and government applications.
“This milestone reinforces our belief that Rockwell Collins is uniquely positioned to produce a navigation receiver that will meet global needs,” said John Borghese, vice president of the Advanced Technology Center for Rockwell Collins. “With decades of experience developing GPS systems and leading edge security architectures, our company continues to be a top innovator in this field.”
More than 35 years ago, Rockwell Collins assisted the U.S. Air Force in developing GPS technology. That legacy continued when the company created the world’s first all-digital miniature GPS receiver under contract with DARPA. Over the years, Rockwell Collins has produced more than 50 GPS products and delivered more than 1 million GPS receivers for commercial avionics and government applications. The GNSS receiver technology being provided for the S-SDR program will continue this legacy of providing leading edge GNSS solutions.
The Canadian Minister of State for science and technology, Ed Holder, visited the University of New Brunswick on July 28 to announce the awarding by the Natural Sciences and Engineering Council of $2.4 million to 28 UNB researchers.
He was joined by Keith Ashfield, member of Parliament for Fredericton, where UNB is based, and Craig Leonard, the New Brunswick Minister of Energy and Mines.
A highlight of the visit was a tour of the Department of Geodesy and Geomatics Engineering to see the work of Prof. Richard Langley and his students. Langley received $170,000 in Natural Sciences and Engineering Research Council (NSERC) funding in the competition. The funding will support the work of his group in improving augmented multi-constellation satellite-based precise positioning in a wide range of environments. Langley is GPS World’s Innovation editor, a post he has held since the magazine’s inception.
Although GPS was the first widely available satellite navigation system, it has now been joined by the Russian GLONASS system, and will soon be accompanied by the European Galileo system, the Chinese BeiDou system, and the Japanese QZSS — all of which have test satellites now in orbit. There are interesting problems to be solved in gaining maximum benefits from this plethora of GNSS for precise positioning and navigation, and Langley and his team will address a number of them.
The team is also involved in the analysis of data from the GPS-based instrument on the Canadian CASSIOPE scientific satellite launched at the end of September 2013. The instrument, which precisely determines the position of the satellite and provides information on the state of the Earth’s ionosphere, was designed at UNB.
The NSERC Discovery Grants Program is an integral component of the government’s efforts to develop, attract and retain the world’s most talented researchers at Canadian universities. The program funds discovery research in a multitude of scientific and engineering disciplines, which builds a broad base of research capacity across the country.
Professor Langley gave the following presentation at the NSERC Discovery Grants Scholarships Rollout Announcement at UNB on July 28:
Two satnav superpowers battled it out aboard a superyacht in the Mediterranean this summer, as a spoofing detector designed to differentiate between real and fake GPS signals came to grips with a spoofing device previously responsible for hijacking a sophisticated drone helicopter, deceiving it into landing when it was trying to hover, and for misdirecting the same luxury yacht in tests last summer.
Mark Psiaki, Cornell University professor of mechanical and aerospace engineering, and graduate student Brady O’Hanlon spent a week aboard the White Rose of Drachs, a luxury superyacht, testing their second-generation spoofing detector as the boat cruised from Monaco around the boot of Italy to Venice at the head of the Adriatic Sea. Also on board was a researcher from assistant professor Todd Humphreys’ Radionavigation Laboratory at the University of Texas at Austin. Humphreys tested his latest spoofer aboard the same yacht last year; this year, Psiaki and O’Hanlon embarked for a follow-up experiment to see if they could outsmart the spoofer.
Both researchers have published earlier versions of their work in GPS World magazine, Psiaki in “GNSS Spoofing Detection,” the Innovation column in the June 2013 issue, and Humphreys in “Drone Hack” in the August 2012 issue.
The former story relates how Humphreys and Psiaki began their investigations as far back as 2008. “There was no intention to help bad actors deceive GNSS user equipment. Rather, our goal was to field a formidable ‘Red Team’ as part of a ‘Red Team/Blue Team’ (foe/friend) strategy for developing advanced ‘Blue Team’ spoofing defenses.”
In international waters this summer, the Cornell and Texas teams could conduct their research unhindered; on land, it’s very difficult to get permission to hack a GPS signal, even for research purposes, Psiaki said.
Aboard the White Rose, Humphreys’ team initiated an attack of the boat’s GPS receiver, overlaying a disguised false signal on top of the real one, and attempting to send the boat off-course without generating any obvious warning signs. Stationed in a different area of the boat, Psiaki and O’Hanlon’s device set itself to detect the false signals through real-time analysis of their properties, and to provide protection against any attack by issuing a definitive warning whenever false signal characteristics were identified.
“We tested numerous spoofing scenarios,” recalled Psiaki. “We proved the efficacy of the new two-antenna version of one of our spoofing detection systems. It is the functional equivalent of our previous moving-antenna spoofing detection system. With two antennas we can simulate the effects of antenna motion without any need for moving parts. The only problems we encountered were with the initial spoofing drag-off, at which point the true and spoofed signals interfere with each other, and signal tracking can be tricky.
“We recorded wide-band data for all these cases. We think that we know how to enhance our defenses to hold on to the signals and recognizing spoofing during the initial drag-off. We also think that we know how to recover the true signals after an attack. The recorded wide-band data should enable us to develop and test these refinements in the lab, i.e., without the need to go back to sea — not that we would mind having to take another cruise on the White Rose of Drachs.”
In one test, the yacht’s GPS receiver was spoofed into believing that it was veering off its course, set northwards to Venice, and heading south to Libya at a very high speed. The Cornell detector was able to warn the White Rose’s bridge crew about the attack before the yacht was 20 meters off course.
“We want to progress to the point where not only can we tell it’s a false signal, but we can also say, ‘Here is the true signal; here is the true position,’” Psiaki added.
The owner of the White Rose of Drachs, an anonymous businessman, allows the boat to be used for scientific purposes during off seasons.
Psiaki will present a paper on the superyacht experiments at the Institute of Navigation’s GNSS+ conference in September in Tampa, Florida, and GPS World will publish an article based on this paper in the November issue.
This story draws on initial reporting by Anne Ju in the July 28 Cornell Chronicle, with additional material and photos supplied by Mark Psiaki.
M3 Systems is now offering the StellaNGC multi-constellation GNSS simulator based on the National Instruments (NI) vector signal transceiver.
The simulator is designed for the testing of satellite navigation receivers for GPS, GLONASS, Galileo, and EGNOS/WAAS. It is designed to improve performance, scalability, and versatility, and reduce cost over existing navigation test solutions.
GNSS is the predominant technology today for navigation and outdoor positioning. However, given the weakness of GNSS signals, receiver performance is often affected by interference from the local environment and propagation channel conditions. Understanding the effects of this interference is of particular importance not only for existing GNSS signals but also for future signals that will appear with the deployment of new constellations such as Galileo.
To properly characterize receiver performance under varying conditions, the StellaGNC multi-constellation GNSS simulator provides signal generation, signal recording and replay, interference generation, signal and data processing, and complete analysis tools. The StellaNGC simulator is based on the NI vector signal transceiver in PXI for improved performance and full simulation capabilities. For record and playback only, a scaled-down version is also available based on the NI USRP (Universal Software Radio Peripheral). Both options were developed with NI LabVIEW and benefit from the performance and flexibility of the NI RF platform.
The simulator provides a scalable solution that allows easy signal additions through software upgrades, multi-frequency, processing extensions with the addition of FPGAs with NI FlexRIO, and an HDD extension for storage increase. Because the simulator is based on the open PXI standard, the hardware investment can also be extended to other applications, such as simulation, record and playback, or payload simulation.
By Roberto Prieto-Cerdeira, Raül Orús-Pérez, Edward Breeuwer, Rafael Lucas-Rodriguez, and Marco Falcone
OFF TO A GOOD START. That’s how we might characterize the European Galileo satellite navigation system. The official beginning of the Galileo program occurred on May 26, 2003, when the European Union and the European Space Agency officially agreed on the first stage of the program (although some work on system concepts took place earlier). The first two prototype or development satellites, Galileo In-Orbit Validation Element-A (GIOVE-A) and GIOVE-B, were launched on December 28, 2005, and April 26, 2008, respectively. The satellites successfully validated key technologies for the full Galileo constellation and secured the system’s frequency allocations.
The first two In-Orbit Validation (IOV) satellites were launched by a single rocket on October 21, 2011, and the third and fourth IOV satellites were similarly launched on October 12, 2012. The two GIOVE satellites and first two IOV satellites provided an opportunity to use Galileo-only receiver measurements and after-the-fact precise satellite orbit and clock data to compute the position of a receiver’s antenna. Joined by two colleagues, I was pleased to report our successful attempt using dual-frequency carrier-phase and pseudorange data collected on May 17, 2012, in an article in the September 2012 issue of this magazine. The two GIOVE satellites were subsequently retired.
The four IOV satellites began transmitting navigation messages with valid ephemerides in March, 2013, and this paved the way for the first real-time single-frequency pseudorange Galileo position fix using the broadcast messages on the morning of March 12 at the Navigation Laboratory of the European Space Research and Technology Centre in Noordwijk, the Netherlands. The position fix included compensation for the effect of the ionosphere on the Galileo signals.
The signals from GNSS satellites travel through the ionosphere on their way to receivers on or near the Earth’s surface. The free electrons populating this region of the atmosphere affect the propagation of the signals, changing their speed and direction of travel. This results in a delay in the arrival of the modulated components of the signals (from which pseudorange measurements are made) and an advance in the phases of the signals’ carrier waves (affecting carrier-phase measurements). The ionosphere is a dispersive medium for radio signals, so by making measurements simultaneously on two frequencies transmitted by a satellite, most of the effect of the ionosphere can be removed. However, single-frequency devices such as most vehicle navigation and handheld receivers don’t have the luxury of dual-frequency correction. These devices must rely on a single-frequency correction model. The coefficients for such a model are included in the navigation messages transmitted by all GPS satellites. Known as the Ionospheric Correction Algorithm or Klobuchar Algorithm, it removes at least 50 percent of the ionosphere’s effect.
The Galileo satellites also include the parameters of an ionospheric algorithm, called NeQuick G, in their navigation messages. In this month’s column, the Galileo system design team describes the novel European way for modeling the ionosphere for single-frequency users and compares its performance to the current GPS approach.
“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. Write to him at lang @ unb.ca.
Radiowave propagation of GNSS signals is affected by the Earth’s atmosphere and the characteristics of the local environment surrounding the receiver. GNSS systems are based on the broadcasting of radiowave ranging signals in the microwave domain (mainly in the so-called L-band, although some new systems like the Indian Regional Navigation Satellite System are also expected to broadcast in the S-band). These electromagnetic signals may suffer from a number of impairments as they propagate from a satellite to a receiver. In considering these effects, we can divide the Earth’s atmosphere into two parts: the electrically neutral atmosphere (primarily the lowest part, the troposphere), whose main effect is a group delay on the navigation signal due to water vapor and the gas components of the dry air, which, for microwave frequencies, is non-dispersive (independent of frequency); and the ionosphere, the ionized part of the atmosphere. The local environment may affect the navigation signal in various ways, too, such as signal fading or complete signal blockage by vegetation or obstacles such as buildings, and multipath, where the signal is broadened in the time and frequency domains due to reflections and diffraction by surrounding objects. In this article, we will discuss the effect of the ionosphere on GNSS signals and how it is being dealt with by the Galileo satellite navigation system.
The ionosphere owes its existence to solar radiation, primarily extreme ultraviolet light. The radiation ionizes the atoms and molecules in the upper atmosphere at heights of less than a hundred kilometers to a few kilometers above the Earth’s surface, producing a sea of ions and free electrons (collectively known as a plasma). This region is responsible for a number of dispersive (frequency-dependent) effects on navigation signals. Chief among these is a persistent delay of the pseudorandom noise (PRN) ranging codes (and the advance of the phase of the underlying carrier waves), thereby introducing positioning and timing errors if not compensated for. Signals are also susceptible to scintillations — rapid variations of amplitude and/or phase of the signals due to diffraction and refraction caused by plasma irregularities. Furthermore, the ionosphere can bend the signal path, resulting in a slightly longer path than the straight path, and rotate the polarization of the signal.
The ionospheric refractive index (the ratio of the speed of propagation of electromagnetic waves in a vacuum to the speed of their propagation in a medium) is related to the number of free electrons along the propagation path. For this purpose, the total electron content (TEC) is defined as the electron density in a cross-section of 1 square meter, integrated along a slant (or vertical) path between two points (such as a satellite and a receiver). It is often expressed in TEC units (TECU) where 1 TECU = 1016 electrons per meter squared = 0.1624 meters of delay at the GPS L1 frequency. According to the electron density, the mechanisms responsible for such ionization, and the dynamics, the ionosphere is usually sub-classified in layers of different characteristics: D, E, F1, and F2, with the latter largely responsible for the ionospheric effects on GNSS.
All of the propagation effects due to the ionosphere depend on a number of factors such as time of the day, location, season, and solar activity. There is also an interaction between solar activity, the ionosphere, and the Earth’s magnetic field, which, at times, can result in a significant disturbance of the ionosphere, as happens during geomagnetic storms. On a long timescale, solar activity follows a periodic, approximately 11-year, cycle. And spatially, the behavior of the ionosphere can be broadly classified into four main regions: the equatorial anomaly regions, located at around ±15-20º on either side of the magnetic equator, usually presenting the largest TEC values; mid-latitude regions, where the daytime TEC values are usually less than half the values found in the equatorial anomaly regions; and the auroral and polar regions, which present moderate TEC values but with larger variability than at mid-latitudes due to the characteristics of the geomagnetic field.
If we ignore some smaller, higher-order terms, the ionospheric group delay (the delay of the “group” of waves making up the PRN ranging code modulations) may be expressed in meters as 40.3 sTEC / f2, where sTEC is slant TEC in electrons per meter squared, calculated along the straight propagation path between receiver and satellite, and f is the carrier frequency in hertz. This effect introduces ranging errors of several meters if not corrected. The higher order terms usually account for differences at the millimeter level (rising to centimeter level during extreme ionospheric disturbances) and may be safely neglected for code ranging. The effect on the carrier phase has the same magnitude as the code delay, but of opposite sign, meaning that the carrier phase is advanced while propagating through the ionosphere. Since the group delay is dispersive, its effect can be mitigated using linear combinations of signals at two separate frequencies.
For single-frequency receivers, GNSSes often rely on correction models driven by broadcast data. For example, with GPS, the Ionospheric Correction Algorithm (ICA, also known as the Klobuchar algorithm) uses eight broadcast coefficients to describe the ionosphere, which is represented as a two-dimensional thin-shell model (the vTEC is assumed to be concentrated in a two-dimensional shell at a given height, relying on an analytical mapping or obliquity function to convert between vTEC and sTEC depending on the elevation angle of the received signal). This model is very efficient in terms of computational complexity, and it usually removes more than 50 percent of the ionospheric error, particularly at mid-latitudes.
Galileo and NeQuick G
Galileo provides dual-frequency services able to mitigate the effects of the ionosphere, but also services to single-frequency users. For a Galileo single-frequency receiver, an algorithm has been developed based on an adaptation of the NeQuick electron density model.
With the launch of the Galileo In-Orbit Validation (IOV) satellites and the initial navigation message broadcast, for the first time the end-to-end performance of the single-frequency correction algorithm for Galileo could be analyzed. The objective of the IOV phase was to launch the first four operational Galileo satellites and to deploy the first version of a completely new ground segment. During this phase, the European Space Agency (ESA) needed to validate — in the operational environment — all space, ground, and user components and their interfaces, prior to full system deployment, including the single-frequency correction algorithm performance starting from April 2013. Results were obtained for the period up to March 2014, coinciding with the maximum of solar cycle 24 and including three equinoxes with increased solar activity. In this article, we present performance results showing that the algorithm is capable of correcting more than 70 percent of the ionospheric group delay error under nominal ionospheric conditions, using only the reduced Galileo infrastructure during IOV (four satellites and a partial set of the Galileo sensor or monitoring stations).
The Algorithm. The Galileo single-frequency correction algorithm is based on an adaptation of the three-dimensional NeQuick electron density model, driven by an effective ionization level calculated with three broadcast ionospheric coefficients.
The original NeQuick model is a three-dimensional and time-dependent ionospheric electron density model based on an empirical climatological representation of the ionosphere, which predicts monthly mean electron density from analytical profiles, depending on solar-activity-related input values: sunspot number or solar flux, month, geographic latitude and longitude, height and UT. It allows us to calculate the TEC through numerical integration of electron density along a path between a beginning and an end point crossing the ionosphere. As an example, a global vTEC map obtained with NeQuick is illustrated in FIGURE 1. The first version of this model (NeQuick1) was incorporated into a previous version of the International Telecommunication Union (ITU) recommendation ITU-R P.532 for TEC estimation in radiowave propagation predictions. Researchers have continued development of the model with updated formulations, and version NeQuick2 is the one currently recommended by the ITU.
The NeQuick model has been adapted for Galileo single-frequency ionospheric corrections (for convenience, the Galileo version is known as NeQuick G) in order to derive real-time predictions based a single input parameter, Az, which is determined using three coefficients broadcast in the navigation message. The three coefficients are used in a second-degree polynomial as a function of the modified dip latitude (MODIP) of the receiver, to determine Az, which replaces the solar flux input parameter of the parent NeQuick model, with the following equation:
(1)
where ai0-2 are the three broadcast coefficients. MODIP is expressed in degrees. A grid table of MODIP values versus geographical location is provided together with the algorithm. A map showing five different MODIP regions is presented in FIGURE 2, each region usually presenting different behavior.
The performance of the Galileo single-frequency ionospheric algorithm, designed to reach a correction capability of at least 70 percent of the ionospheric code delay, had been assessed in the past using GPS data only and using GPS plus Galileo In-Orbit Validation Element satellite data for an offline estimation of the broadcast parameters.
Since the first successful autonomous real-time Galileo-based position fix on March 12, 2013, the Galileo navigation messages have been broadcast by the four IOV spacecraft to the external user community, including the ionospheric broadcast parameters determined with IOV-only observations.
Experiment Period and Performance Indicators
To analyze the performance of the single-frequency ionospheric correction, a number of performance indicators were used:
(2)
where STECref is the reference STEC and STECNeQuickG is the STEC obtained with the Galileo correction model. The factor 66 is used to avoid the fact that small absolute errors, which are relatively large due to small reference values, inflate the correction capability; it is linked to a target correction of 70 percent with a minimum absolute threshold of 20 TECU (30 percent of 66 TECU is about 20 TECU).
Performance verification has been assessed for the period from April 2013 to March 2014, which includes the secondary peak of the current solar maximum. The Galileo broadcast data used for this test are the Az coefficients broadcast by the four Galileo IOV satellites. It is important to remember that during the period of this assessment, the IOV infrastructure was reduced with respect to the target full operational capability, including the generation of the ionospheric parameters: four IOV satellites (no other GNSS satellites were used in the estimation) and a reduced number of monitoring stations.
Since the ionospheric correction performance assessment can be done independently of the Galileo signals and analysis of performance is preferred over independent data and locations, reference STEC estimated using dual-frequency observables from GPS at stations from the International GNSS Service (IGS), distributed around the world, were selected for the correction capability performance assessment. This resulted in observations of six to nine satellites for any epoch and with more than 120 stations per day, which assured good global coverage for the test. Performance has been computed individually for each set of broadcast parameters. For this aspect of ionospheric correction assessment, the differences between GPS and full constellation Galileo geometries are considered to be negligible.
As a reference for comparative purposes, for some cases the results have been compared to those obtained with the GPS ICA correction model using the broadcast parameters from GPS satellites.
The reference ionosphere STEC values were computed using dual-frequency carrier-phase GPS observables from IGS stations at a sampling rate of 300 seconds, and using IGS final global ionospheric maps (GIMs) to level the geometry-free combination of carrier phases. In this context, the IGS GIMs are employed to align the geometry-free or ionospheric combination, LI, to compute the ambiguity term (BI) for each satellite-to-receiver arc:
(3)
where LI represents the linear combination between signals at frequencies f1 and f2; 
is the ionospheric delay in meters of LI; and BI is composed of several terms: station and satellite phase inter-frequency biases (
and 
respectively), LI phase ambiguity (λ1N1j – λ2N2j), phase wind-up, multipath, and noise. And i corresponds to the station and j to the satellite.
Then, in order to compute the corresponding BI term for each satellite-receiver continuous arc, the sTEC prediction of the GIM (sTECGIM_map) is computed for each satellite ionospheric pierce point, and then the average is computed as follows:
(4)
where the indices i, j, and α correspond to the receiver, satellite, and arc indicator respectively, and the average is performed over the corresponding continuous (no cycle slips) arc (α) of data. 
is estimated following the mapping function and the procedures to interpolate in space and time recommended by IGS for GIM maps represented in ionosphere-exchange (IONEX) format.
With this estimation, the aligned STEC can be obtained as:
(5)
which is the STEC used as an accurate sTEC estimation or “truth” reference value.
Results
The first analysis that we performed was the daily RMS error and correction capability for all stations. Most days have shown very promising performance. To see different levels of performance, results for one “bad” day and one typical “good” day, in the period of experimentation, are presented in FIGURE 3. It is observed that even for the “bad” day, the correction capability is above 70 percent, except for some stations in the equatorial regions. This performance is exceeded significantly for the “good” day, with RMS residual ionospheric errors below 1.5 meters for L1 even at low latitudes.
The evolution of the RMS residual error both for Galileo NeQuick G and GPS ICA from April 2013 to March 2014 are presented in FIGURE 4. In this figure, ionospheric activity at the equinoxes is clearly observed in the degradation of performance, and the influence of increased solar activity from October 2013 to March 2014 is also evident.
The residual error of the Galileo correction model is already at the level of the expected capability for the full constellation. It also shows better performance as compared to the GPS ICA model, especially at equatorial latitudes.
The level of correction capability for each station for the Galileo NeQuick G model and the GPS ICA model are presented in FIGURE 5 for a quiet day in May 2013 and an active day during the spring equinox in 2014.
Effect in the Positioning Domain. We have performed two analyses to assess the correction performance in the positioning domain: one using GPS observables and one with Galileo-only observables. In both cases, we used three ionospheric delay mitigation methods: the dual-frequency ionosphere-free combination, the single-frequency GPS ICA correction algorithm, and the single-frequency Galileo NeQuick G correction algorithm.
The performance of the correction algorithm in the positioning domain using GPS observables was performed with data from two stations: Noordwijk in The Netherlands (a mid- to high-latitude station) and Malindi in Kenya (a low-latitude station) for the day of year (doy) 172 of 2013. Results are presented in FIGURES 6 and 7 showing good performance of the NeQuick G correction, in particular at low latitude. The results do not include code smoothing neither for single-frequency nor dual-frequency positioning. In the results, it may be observed that, as expected, the noise level for single-frequency positioning is much lower than that of ionosphere-free, but a higher bias may be present (the residual mean ionospheric error).
Positioning domain analysis with Galileo-only observations using the four Galileo IOV satellites, and applying the NeQuick G correction, was evaluated for a station in Washington, D.C., for doy 245, 2013, including E1-only, E5a-only, and dual-frequency E1-E5a ionosphere-free observations. (E1 is centered at the GPS L1 frequency, while E5a is centered at the GPS L5 frequency.) These results are presented in FIGURE 8. The single-frequency positioning performance is considered promising considering the limited number of satellites.
Conclusions
The performance of the Galileo single-frequency ionospheric correction algorithm, based on NeQuick G, was evaluated using the broadcast navigation messages from the four Galileo IOV satellites, both in correction capability and in the positioning domain for the period April 2013 to March 2014. Despite the reduced infrastructure (broadcast ionospheric parameters estimated using only the IOV satellites at a limited number of monitoring stations), the performance shows promising results, in particular for low-latitude regions where the ionosphere is more problematic and, as expected, it has been confirmed that the correction performance is correlated with solar activity.
Acknowledgments
The NeQuick electron density model was developed by the Abdus Salam International Center of Theoretical Physics in Trieste, Italy, and the University of Graz in Austria. The adaptation of NeQuick for the Galileo single-frequency ionospheric correction algorithm (NeQuick G) was performed by ESA and involved the original developers of NeQuick and other European ionospheric scientists under various ESA projects.
Note to Manufacturers
The publication of the NeQuick G model and the Galileo single-frequency correction algorithm is under preparation for public release by the European Commission.
ROBERTO PRIETO-CERDEIRA is a propagation engineer in the European Space Agency (ESA) at the European Space Research and Technology Centre (ESTEC) in Noordwijk, The Netherlands, responsible for the activities related to radiowave propagation for GNSS and satellite mobile communications.
RAUL ORUS-PEREZ is a propagation engineer at ESTEC, working on activities related to radiowave propagation in the troposphere and ionosphere for GNSS and other ESA projects.
EDWARD BREEUWER is the system integration and verification manager in the Galileo Project Office at ESTEC, responsible for the organization and coordination of all testing activities at the system level. He had overall responsibility for the IOV test campaign.
RAFAEL LUCAS-RODRIGUEZ is the Galileo Services Engineering Manager for the Galileo project at ESTEC.
MARCO FALCONE is the System Manager in the Galileo Project Office at ESTEC.
• Development of NeQuick Ionospheric Model
“A New Version of the NeQuick Ionosphere Electron Density Model” by B. Nava, P. Coïsson, and S.M. Radicella in Journal of Atmospheric and Solar-Terrestrial Physics, Vol. 70, No. 15, December 2008, pp. 1856–1862, doi: 10.1016/j.jastp.2008.01.015.
“A Family of Ionospheric Models for Different Uses” by G. Hochegger, B. Nava, S.M. Radicella, and R. Leitinger in Physics and Chemistry of the Earth, Part C: Solar, Terrestrial & Planetary Science, Vol. 25, No. 4, 2000, pp. 307–310, doi : 10.1016/S1464-1917(00)00022-2.
“An Analytical Model of the Electron Density Profile in the Ionosphere” by G. Di Giovanni and S.M. Radicella in Advances in Space Research, Vol. 10, No. 11, 1990, pp. 27–30, doi: 10.1016/0273-1177(90)90301-F.
• Evaluation of the Galileo Single-Frequency Ionospheric Model
“Assessment of NeQuick Ionospheric Model for Galileo Single-Frequency Users” by A. Angrisano, S. Gaglione, C. Giola, M. Massaro, and U. Robustelli in Acta Geophysica, Vol. 61, No. 6, December 2013, pp. 1457–1476, doi: 10.2478/s11600-013-0116-2.
Ionosphere Modelling for Galileo Single Frequency Users by B. Bidaine, Ph.D. thesis, Université de Liège, Liège, Belgium, October 2012.
“GIOVE-A Experimentation Campaign: Ionospheric Related Data Analysis” by R. Orus and R. Prieto-Cerdeira in Proceedings of NAVITEC 2008, the 4th ESA Workshop on Satellite Navigation User Equipment Technologies: GNSS User Technologies in the Sensor Fusion Era, Noordwijk, The Netherlands, December 10–12, 2008.
“Assessment of the Ionospheric Correction Algorithm for GALILEO Single Frequency Receivers” by R. Prieto-Cerdeira, R. Orus, and B. Arbesser-Rastburg in Proceedings of NAVITEC 2006, the 3rd ESA Workshop on Satellite Navigation User Equipment Technologies, Noordwijk, The Netherlands, December 11–13, 2006.
“Advanced Ionospheric Modelling for GNSS Single Frequency Users” by M.A Aragón Ángel and F. Amarillo Fernández in the Proceedings of PLANS 2006, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium, San Diego, California, April 24–27, 2006, pp. 110–120, doi: 10.1109/PLANS.2006.1650594.
• GPS Ionospheric Model
“Ionospheric Time-delay Algorithm for Single-frequency GPS Users” by J.A. Klobuchar in IEEE Transactions on Aerospace and Electronic Systems, Vol. AES-23, No. 3, May 1987, pp. 325–331, doi: 10.1109/TAES.1987.310829
“Ionospheric Effects on GPS” by J.A. Klobuchar in GPS World, Vol. 2, No. 4, April 1991, pp. 48–51.
• Ionospheric Effects on GNSS
“GPS, the Ionosphere, and the Solar Maximum” by R.B. Langley in GPS World, Vol. 11, No. 7, July 2000, pp. 44–49.
• International GNSS Service Ionosphere Map Exchange Format
IONEX: The IONosphere Map EXchange Format Version 1 by S. Schaer, W. Gurtner, and J. Feltens, February 25, 1998.
A new way of measuring sea level using satellite navigation system signals, for instance GPS, has been implemented by scientists at Chalmers University of Technology in Sweden. Sea level and its variation can easily be monitored using existing coastal GPS stations, the scientists have shown.
Measuring sea level is an increasingly important part of climate research, and a rising mean sea level is one of the most tangible consequences of climate change. Researchers at Chalmers University of Technology have studied new ways of measuring sea level that could become important tools for testing climate models and for investigating how the sea level along the world’s coasts is affected by climate change.
Johan Löfgren and Rüdiger Haas, scientists at Chalmers Department of Earth and Space Sciences, have developed and tested an instrument that measures the sea level using a GNSS tide gauge.
”The global mean sea level is rising because of climate change, but the change depends on where you are in the world,” says Rüdiger Haas. “We want to be able to make detailed measurements of sea level so that we can understand how coastal societies will be affected in the future.”
The GNSS tide gauge uses GPS and GLONASS signals. BeiDou and Galileo will be added in the future.
”We measure the sea level using the same radio signals that mobile phones and cars use in their satellite navigation systems,” says Johan Löfgren. “As the satellites pass over the sky, the instrument ‘sees’ their signals — both those that come direct and those that are reflected off the sea surface.”
Two antennas, covered by small white radomes, measure signals both directly from the satellites and signals reflected off the sea surface. By analyzing these signals together, the sea level and its variation can be measured, up to 20 times per second. The sea level time series is rich in physical phenomena such as tides (caused mostly by the gravitational pull of the Moon and the Sun), meteorological signals (high and low pressure), and signals from climate change. Through advanced signal processing, these signals can be studied further.
The new GNSS tide gauge can measure changes in both land and sea at the same time, in the same location. That means both long-term and short-term land movements (post-glacial rebound and earthquakes) can be taken into consideration.
”Now we can measure the sea level both relative to the coast and relative to the center of the Earth, which means we can clearly tell the difference between changes in the water level and changes in the land,” says Johan Löfgren.
This summer, other high-precision instruments will be installed to work with the Onsala GNSS tide gauge, in collaboration with SMHI, the Swedish Meteorological and Hydrological Institute.
”Our tide gauge station will become part of a network of stations along the coast of Sweden that will be able to monitor changes in the water level to millimeter precision well into the future,” says Gunnar Elgered, professor at Chalmers Department of Earth and Space Sciences.
The scientists have also shown that existing coastal GNSS stations, installed primarily for the purpose of measuring land movements, can be used to make sea-level measurements.
”We’ve successfully tested a method where only one of the antennas is used to receive the radio signals. That means that existing coastal GNSS stations — there are hundreds of them all over the world — can also be used to measure the sea level,” says Johan Löfgren.
More about the research
The method is described in two new scientific articles:
and Sea level measurements using multi-frequency GPS and GLONASS observations
This work was previously reported in these publications:
Larson, K.M., J. Lofgren, and R. Haas, Coastal Sea Level Measurements Using A Single Geodetic GPS Receiver, Adv. Space Res., Vol. 51(8), 1301-1310, 2013, doi:10.1016/j.asr.2012.04.017, 2013.
Larson, K.M., R. Ray, F. Nievinski, and J. Freymueller, The Accidental Tide Gauge: A Case Study of GPS Reflections from Kachemak Bay, Alaska, IEEE GRSL, Vol 10(5), 1200-1205, doi:10.1109/LGRS.2012.2236075, 2013.
Did you get a fix on four Galileo satellites? Then there could be a certificate in it for you! ESA will recognize Galileo pioneers with commemorative certificates to the first 50 entities who document their achievement of a past or present fix. Details of how to apply are provided here.
To mark the first anniversary of Galileo’s historic first satnav positioning measurement, ESA plans to award certificates to groups who picked up signals from the four satellites in orbit to perform their own fixes.
In 2011 and 2012 the first four satellites were launched — the minimum number needed for navigation fixes.
On March 12, 2013, Galileo’s space and ground elements came together for the first time to perform the historic first determination of a ground location — the Navigation Laboratory of ESA’s Technical Centre in Noordwijk, the Netherlands.
From this point, generation of navigation messages enabled full testing of the entire Galileo system — not just by ESA and its industry and institutional partners but also by any entity with a customized satnav receiver.
ESA’s Galileo team has heard about position fixes carried out by organizations and companies all over Europe and beyond, including as far away as Vietnam.
A year after the first fix, ESA is recognizing these Galileo pioneers with commemorative certificates to the first 50 entities who document their achievement of a past or present fix.
Applicants should send in their name, address, details of the receiver they used, the start and end time of their fixes in Universal Time Coordinated (UTC) and a plot of their latitude/longitude position fixes overlaid on a map, such as Google Earth. Submissions should be sent to [email protected] within the next two months. Certificates will be sent out after May 12, along with an online results update. See details of how to apply here.
The first Galileo services are scheduled to begin later this year, as more satellites are delivered into orbit. The next launches will occur in the second half of this year, each with two satellites aboard a Soyuz ST-B. They will take place in close succession to build up the constellation.
Many satnav receiver chips are already technically Galileo ready, requiring only software upgrades from their manufacturer to begin working with Galileo signals along with GPS and other international satnav systems.
Spirent Communications, a testing navigation and positioning systems company, today announced the introduction of Spirent SimSAFE, a software solution that concurrently simulates legitimate Global Navigation Satellite System (GNSS) constellations and spoofed or hoax signals to evaluate receiver resilience and help develop counter measures. SimSAFE was developed in conjunction with Qascom, GNSS signal security and authentication experts.
As GNSS become increasingly embedded in modern infrastructure for application timing and device positioning, the opportunities for interference and spoofing attacks become greater, Spirent said. Hoax or spoofing attacks work by mimicking genuine GNSS signals, which mislead GNSS receivers. From mobile telephony to Internet banking, GNSS timing signals are used in many key systems, and yet there is no requirement on GNSS equipment to demonstrate any degree of robustness to block or even detect malicious attacks that disrupt performance. Often, affected receivers do not recognize when they are receiving fake signals and continue to operate normally, but provide false time or position information.
“GNSS signal vulnerability is becoming a significant issue,” said John Pottle, marketing director of Spirent’s Positioning Division. “SimSAFE is the first tool to help develop systems that will detect and counter spoofing attacks. This solution is unique in being able to provide a means of both emulating a spoof attack and monitoring a receiver under attack to evaluate mitigation strategies and countermeasures.”
SimSAFE is a fully controllable laboratory-based, non-radiated test solution to evaluate a receiver’s response to a wide range of spoofing attacks. The test tool generates simulated spoofing attacks that can be aligned with genuine signals from an antenna or locally generated “genuine” signals using a Spirent GNSS simulator. This allows users to simulate a wide range of sophisticated attacks, monitor the response of the receiver under attack and evaluate the effectiveness of proposed countermeasures to then improve resilience against such attacks.
In essence Spirent’s SimSAFE spoofing test bed does two things:
The two principal applications of SimSAFE are:
Encouraging results from new indoor tests and advances in collaborative phased arrays come from MUSTER: multiple independently operating GPS receivers that exchange their signal and measurement data to enhance GNSS navigation in degraded signal environments, such as urban canyons and indoors.
By Andrey Soloviev and Jeffrey Dickman
Bringing GNSS navigation further indoors by adding new users to a collaborative network can help realize the concept of ubiquitous navigation. Increasing the number of receiver nodes to improve signal-to-noise ratios and positioning accuracy lies at the heart of the MUlti-platform Signal and Trajectory Estimation Receiver (MUSTER). This article focuses on benefits of integrating multi-node receiver data at the level of signal processing, considering two case studies:
MUSTER organizes individual receiver nodes into a collaborative network to enable:
To exclude a single point of failure, the receiver network is implemented in a decentralized fashion. Each receiver obtains GNSS signals and signal measurements (code phase, Doppler shift and carrier phase) from other receivers via a communication link and uses these data to operate in a MUSTER mode (that is, to implement a multi-platform signal fusion and navigation solution). At the same time, each receiver supplies other receivers in the network with its signal and measurement data. Figure 1 illustrates the overall system architecture.
Open-loop tracking is the key technological enabler for multi-node signal processing. Particularly, MUSTER extends an open-loop tracking concept that has been previously researched for single receivers to networked GNSS receivers. Signals from multiple platforms are combined to construct a joint 3D signal image (signal energy versus code phase and Doppler shift). Signal parameters (code phase, Doppler shift, carrier phase) are then estimated directly from this image and without employing tracking loops.
Open-loop tracking is directly applied to accommodate limitations of military and civilian data links. To support the functionality of the receiver network at the signal processing level (that is, to enable multi-platform signal tracking and multi-platform phased arrays) while satisfying bandwidth limitations of existing data link standards, individual receivers exchange pre-correlated signal functions rather than exchanging raw signal samples.
Before sending its data to others, each receiver processes the incoming satellite signal with a pre-processing engine. This engine accumulates a complex amplitude of the GNSS signal as a function of code phase and Doppler frequency shift. Receivers then broadcast portions of their pre-correlated signal images that are represented as a complex signal amplitude over the code/Doppler correlation space for 1-ms or 20-ms signal accumulation. For broadcasting, portions of signal images are selected around expected energy peaks whose locations are derived from some initial navigation and clock knowledge.
This approach is scalable for the increased number of networked receivers and/or increased sampling rate of the ranging code (such as P(Y)-code vs. CA-code). The link bandwidth is accommodated by tightening the uncertainty in the location of the energy peak. As a result, the choice of the data link becomes a trade-off between the number of collaborative receivers and MUSTER cold-start capabilities (that is, maximum initial uncertainties in the navigation and clock solution).
Multi-Node Signal Accumulation
An earlier paper that we presented at the ION International Technical Meeting, January 2013, describes the approach of multi-platform signal accumulation for those cases where relative multi-node navigation and clock states are partially known. This section reviews that approach and then extends it to cases of completely unknown relative navigation and clock states. The following assumptions were previously used:
These assumptions are generally suitable for a pedestrian type of receiver network (such as a group of cellular phone users in a shopping mall area) where individual nodes stay within 100 meters from each other; their relative velocities do not differ by more than 2 meters/second; and, the clocks can be pre-calibrated using communication signals. In this case, zero relative states are used for the multi-node signal accumulation and subsequent tracking. Figure 2 summarizes the corresponding MUSTER tracking architecture.
Relative navigation states are initialized based on clock calibration results only: zero relative position and velocity are assumed. These initial states are then propagated over time, based on MUSTER/supplemental tracking results (Doppler frequency estimates and higher-order Doppler terms). Code and frequency tracking states are computed by combining biased and unbiased measurements. Biased measurements are obtained by adjusting supplemental signal images for approximately known relative states only. Unbiased measurements are enabled by relative range/Doppler correction algorithms that estimates range and frequency adjustments for each supplemental receiver.
The Kalman filter that supports the optimal combination of biased and unbiased tracking measurements also includes code-carrier smoothing to mitigate noise in measured code phase. For those cases where multi-platform signals are combined coherently, a standard carrier-smoothing approach is used. When non-coherent signal combinations are applied, a so-called pseudo-carrier phase is first derived by integrating Doppler estimates over time and then applied to smooth the code phase.
Multi-platform signal accumulation and tracking can be extended to include cases where the relative navigation parameters are completely unknown. For such cases, MUSTER implements an adjustment search to find the values of code phase and Doppler shift for each supplemental receiver that maximize the overall signal energy.
Adjustment search must be implemented if MUSTER/supplemental relative states are completely unknown, or if their accuracy is insufficient to enable direct accumulation of multi-platform energy, for example, when the relative range accuracy is worse than 150 meters and an energy loss of at least 3 dB is introduced to the signal accumulation process. For each code phase, Doppler and carrier phase (if coherent integration is performed) from the adjustment search space, a supplemental 1-ms function is adjusted accordingly and then added to the MUSTER function. Multiple 3D GPS signal images are constructed, and the image with the maximum accumulated energy is applied to initialize relative navigation parameters: code phase and Doppler shift adjustments values from the adjustment search space that correspond to the energy peak serve as approximate estimates of relative range and Doppler.
The accuracy of these estimates is defined by the resolution of the adjustment search, which would be generally kept quite coarse in order to minimize the search space. For instance, a 300-meter search grid is currently implemented for the code phase, which enables the resolution of relative ranges within 150 meters only. Hence, to mitigate the influence of relative state uncertainties on the tracking quality, a correction algorithm is applied as described in our earlier paper. Figure 3 shows the overall system architecture.
The architecture keeps all the previously developed system components and adds the adjustment search capability (red block in Figure 3) to incorporate cases of unknown MUSTER/supplemental receivers’ relative navigation states. To minimize the computational load, adjustment search is performed only for the first tracking epoch. Search results are applied to initialize the estimates of MUSTER/supplemental range and Doppler, which are then refined at each subsequent measurement epoch using a combined biased/noisy tracking scheme.
The updated architecture can support cases of completely unknown relative states, as well as those cases where relative states are coarsely known, but this knowledge is insufficient to directly combine multi-platform signals.
The complete adjustment search is possible. However, it is extremely challenging for actual implementations due to both large computational load and a data exchange rate associated with it. To exemplify, NcodexNDoppler versions of the multi-platform 3D function have to be computed for the case where Ncode code phase and NDoppler Doppler shift adjustment search bins are used and outputs from two receivers are combined non-coherently. A complete search (1023 code bins and 11 frequency bins) requires computation of 11,253 3D functions. This number increases to (11,253)2 or 126,630,009 if the third receiver is added.
In addition, receivers must exchange their complete pre-correlated signal functions, which puts a considerable burden on the computational data link. For instance, the exchange of complete 1-ms functions with the 4-bit resolution of samples (required to track the carrier phase) results in the 45 Mbit/s data rate for only a 2-receiver network. Hence, it is anticipated that for practical scenarios, a reduced adjustment search will be utilized for cases where the accuracy of relative states does not support the direct accumulation of multi-platform signals: for example, when the distance between users in the network exceeds 150 meters. In this case, only segments of 1-ms functions around expected energy peaks (estimated based on approximate navigation knowledge) are exchanged.
Phased Arrays
Multi-platform phased arrays have been developed to enable interference and jamming protection for GNSS network users who cannot afford a controlled reception pattern antenna (CRPA) due to size, weight, and power (SWAP), as well as cost constraints. The multi-node phased array approach presented here cannot match the performance of CRPA, with its careful design, antenna calibration, and precise knowledge of relative location of phase centers of individual elements. However, it can still offer a significant interference protection to networked GNSS users.
The multi-platform phased array implements a cascaded space-time adaptive processing (STAP) as illustrated in Figure 4.
Cascaded STAP implements temporal filtering at a pre-correlation stage, while spatial filtering (in a form of the digital beam forming or DBF) is carried out at post-correlation. Cascaded STAP is implemented instead of joint STAP formulation to
Signal samples are still exchanged for the estimation of signal covariance matrices that are required for the computation of temporal and spatial weights. However, the sample exchange rate is reduced significantly as compared to the joint STAP: for example, only 100 samples are currently being exchanged out of the total of 5000 samples over a 1-ms signal accumulation interval.
The DBF uses the Minimum Variance Distortion-less Response (MVDR) formulation for the computation of spatial weight vector. MVDR constrains power minimization by the undisturbed signal reception in the satellite’s direction:
(1)
where Φ is the multi-node signal covariance matrix that is computed based on temporal filter outputs; superscript H denotes the transpose and complex conjugate operation; and, η is the steering vector that compensates for phase differences between array elements for the signal coming from the satellite’s direction:
(2)
In (2), u is the receiver-to-satellite line-of-sight (LOS) unit vector; rm is the relative position vector between phase centers of the mth node and MUSTER; (,) is the vector dot product; and, λ is the carrier wavelength.
Following computation of DBF weight, multi-node 1-ms GPS signal functions are combined:
(4)
where 
is the complex 1-ms accumulated signal amplitude of the mth node for the (l,p) bin of the code/carrier open-loop tracking search space. The result is further accumulated (for example, over 20 ms) and then applied for the open-loop estimation of signal parameters.
One of the most challenging requirements of the classical MVDR-based DBF is the necessity to estimate relative multi-node position and clock states at a centimeter level of accuracy. To eliminate this requirement and extend potential applications of multi-node phased arrays, the DBF was modified as illustrated in Figure 5.
The modified approach searches through phase adjustments to supplemental receivers and chooses the adjustment combination that maximizes the output carrier-to-noise ratio (C/N0). As a result, no knowledge of the relative navigation states is needed. For each phase combination, 
, from the adjustment search space, the satellite lookup constraint is computed as:
(5)
Due to the cyclic nature of the phase, the search space is limited to the [0,2π] region. The search grid resolution of π/2 is currently being used.
The obvious drawback of the exhaustive search-based DBF is that the approach is not scalable for the increased number of network users. However, it can still be efficiently applied to a relatively limited network size such as, for example, five collaborative receivers. In addition, the method does not generally support interference suppression with carrier-phase fidelity. However, code and Doppler frequency tracking statuses are still maintained as it is demonstrated in the next section using experimental results.
Experimental Results
We used two types of experimental setups as shown in Figures 6 and 7, respectively.
The first setup (Figure 6) was used to demonstrate multi-platform signal accumulation with unknown relative states and multi-node phased arrays. Raw GPS signals received by three antennas were acquired by a multi-channel radio-frequency (RF) front-end and recorded by the data collection server. The first antenna served as the MUSTER platform, the second and third antennas were used as supplemental platforms. Relative antenna locations were measured as [-0.00; 0.99; 0.05] m (East, North, Up components) for the MUSTER/supplemental receiver 1; and, [0.16; 0.76; 0.27] m for the MUSTER/supplemental receiver 2.
A stationary test scenario was considered. Clock biases were artificially induced to emulate a case of asynchronous network. Clock biases were introduced by converting raw GPS signal samples into the frequency domain (applying a fast Fourier transform (FFT) to 1-ms batches of signal samples); implementing a frequency-domain timing shift; and, converting shifted signals back into the time domain (via inverse FFTs). Multi-platform signal processing algorithms were then applied to raw GPS signals with asynchronous multi-platform clocks.
The second setup (Figure 7) was applied for the demonstration of indoor signal tracking. Two receiver nodes (roof and cart) with independent front-ends were used. The roof node remained stationary, while the cart was moved indoors. Each node in the data collection setup includes a pinwheel GPS antenna, an RF front-end, an external clock for the front-end stabilization, and a data collection computer. Figure 7 illustrates corresponding test equipment for the cart node.
Multi-Platform Signal Tracking with Unknown Relative States. Two platforms were used to demonstrate the case of completely unknown states (antennas 1 and 3 in Figure 6). The third platform was not used due to the extreme computational burden of the complete adjustment search (about 106 grid points for the case of three platforms). A 0.2-ms (60 km) clock bias was added to GPS signal samples recorded by antenna 3. Complete adjustment search was implemented for the code phase. No adjustment search was needed for the Doppler shift. The use of adjustment search provides approximate estimates of relative shifts in multi-platform code phases. These approximate estimates are then refined using a relative range estimation algorithm. Figures 8 and 9 exemplify experimental results for cases of coherent (C/N0 is 31 dB-Hz) and non-coherent (C/N0 is 29 dB-Hz) multi-platform signal accumulation.
Consistent code- and carrier-phase tracking is maintained for the coherent accumulation case.
Carrier-phase and code-phase error sigmas were estimated as 8.2 mm and 28.8 meters, accordingly. The carrier-smoothed code tracking error varies in the range from –4 to –2 meters for the steady-state region. For the non-coherent tracking case, errors in the carrier smoothed code measurements stay at a level of –5 meters. These example test results validate MUSTER tracking capabilities for the case of completely unknown relative navigation states.
Indoor Signal Processing
The indoor test was performed to demonstrate the ability of MUSTER to maintain signal tracking status under extreme signal attenuation conditions. The test was carried out at the Northrop Grumman campus in Woodland Hills, California, with no window view for the entire indoor segment; all the received GPS signals were attenuated by the building structure. Raw GPS signal data was collected from the test setup shown in Figure 6 and then post-processed with multi-platform signal accumulation algorithm with partially known relative navigation states. A combined 20-ms coherent/0.2-s non-coherent signal accumulation scheme was applied. A complete position solution was derived from five highest-elevation satellites.
As the results for the indoor test show in Figure 10, MUSTER supports indoor positioning capabilities for the entire test trajectory. The GPS-only indoor solution reconstructs the right trajectory shape and size. Solution discontinuities are still present. However, the level of positioning errors (20 meters is the maximum estimated error) is lowered significantly as compared to traditional single-node high-sensitivity GPS implementations where errors at a level of hundreds of meters are commonly observed. This accuracy of the multi-node solution can be improved further when it is integrated with other sensors such as MEMS inertial and vision-aided navigation.
Multi-Platform Phased Arrays
For the functionality demonstration of multi-platform phased arrays, live GPS signal samples were collected with the test setup shown in Figure 6. Interference sources were then injected in software including continuous wave (CW) and matched spectrum interfering signals. The resultant data were post-processed with the multi-platform phased array approach described above. Relative navigation and clock states were unknown; the DBF formulation was augmented with the phase adjustment search.
Figures 11 and 12 exemplify experimental results.
Test results presented demonstrate consistent GPS signal tracking for jamming-to-signal (J/S) ratios from 50 to 55 dB. The steady-state error in the carrier-smoothed code is limited to 5 meters.
Acknowledgment
This work was funded, in part, by the Air Force Small Business Innovation Research (SBIR) grant, Phase 1 and Phase 2, topic number AF103-185, program manager Dr. Eric Vinande.
Andrey Soloviev is a principal at Qunav. Previously he served as a Research Faculty at the University of Florida and as a Senior Research Engineer at the Ohio University Avionics Engineering Center. He holds B.S. and M.S. degrees in applied mathematics and physics from Moscow Institute of Physics and Technology and a Ph.D. in electrical engineering from Ohio University.
Jeff Dickman is a research scientist with Northrop Grumman Advanced Concepts and Technologies Division. His area of expertise includes GPS baseband processing, integrated navigation systems, and sensor stabilization. He holds a Ph.D. in electrical engineering from Ohio University. He has developed high-accuracy sensor stabilization technology and is experienced with GPS interferometry for position and velocity aiding as well as high-sensitivity GPS processing techniques for challenging GPS signal conditions.
The 864-channel TRE-3 receiver, just announced by JAVAD GNSS, can simultaneously access all current GNSS signals, with room to spare for multiple-channel tracking of select signals, according to the company. The new product offers many features, including:
TRE-3 is form, pin-out, and command compatible with the company’s earlier TRE-G3T receiver. It uses 8-Watts of power, compared to 4-Watts of the TRE-G3T
The European Space Agency’s Galileo satellites have achieved their first aerial fix of longitude, latitude and altitude, enabling the inflight tracking of a test aircraft. ESA’s four Galileo satellites in orbit have supported months of positioning tests on the ground across Europe since the first fix in March.
Now the first aerial tracking using Galileo has taken place, marking the first time that Europe has been able to determine the position of an aircraft using only its own independent navigation system. The milestone took place on a Fairchild Metro-II above Gilze-Rijen Air Force Base in the Netherlands at 12:38 GMT on November 12. It was part of an aerial campaign overseen jointly by ESA and the National Aerospace Laboratory of the Netherlands, NLR, with the support of Eurocontrol, the European Organisation for the Safety of Air Navigation, and LVNL, the Dutch Air Navigation Service Provider.
A pair of Galileo test receivers was used aboard the aircraft, the same kind employed for Galileo testing in the field and in labs across Europe. They were connected to an aeronautical-certified triple-frequency Galileo-ready antenna mounted on top of the aircraft.
Tests were scheduled during periods when all four Galileo satellites were visible in the sky – four being the minimum needed for positioning fixes. The receivers fixed the plane’s position and, as well as determining key variables such as the position, velocity and timing accuracy; time to first fix; signal-to-noise ratio; range error; and range–rate error.
Testing covered both Galileo’s publicly available Open Service and the more precise, encrypted Public Regulated Service, whose availability is limited to governmental entities.
Flights covered all major phases: take off, straight and level flight with constant speed, orbit, straight and level flight with alternating speeds, turns with a maximum bank angle of 60º, pull-ups and push-overs, approaches and landings.
They also allowed positioning to be carried out during a wide variety of conditions, such as vibrations, speeds up to 456 km/h, accelerations up to 2 ghorizontal and 0.5–1.5 gvertical, and rapid jerks. The maximum altitude reached during the flights were 3000 m.
NLR’s Fairchild Metro-II has previously performed initial European GPS testing in the 1980s, and the first tests of the European Geostationary Navigation Overlay Service, EGNOS, which sharpens GPS accuracy and monitors its reliability over Europe for high-accuracy or even safety-of-life uses.
The definition and development of Galileo’s in-orbit validation phase were carried out by ESA and co-funded by ESA and the EU.
The Full Operational Capability phase is managed and fully funded by the European Commission. The Commission and ESA have signed a delegation agreement by which ESA acts as design and procurement agent on behalf of the Commission.
