Delegates from the UN’s International Committee Global Navigation Systems (UN ICG) at the entrance to ESA’s ESTEC Test Centre, used to test the last 22 Galileo satellites. (Photo: ESA)
The UN ICG group visited ESTEC on May 16 during a meeting in the Netherlands.
News from the European Space Agency (ESA)
Members of the United Nations (UN) technical group supporting global cooperation in satellite navigation toured ESA’s technical centre in the Netherlands to see key facilities used to develop Europe’s Galileo system.
Delegates from the UN’s International Committee on Global Navigation Systems (UN ICG) met in mid-May at the nearby Galileo Reference Centre, operated by the GSA, European Global Navigation Satellite Systems Agency.
ESA, one of the founding members of the ICG in 2005, invited them to visit the agency’s European Space Research and Technology Centre, ESA’s single largest establishment and home to its Navigation Directorate.
Javier Benedicto, ESA’s Galileo program manager was joined by Rodrigo Da Costa, GSA’s Head of Exploitation, in giving the visitors a hearty welcome. “I’m honored to work with the amazing team of engineers and managers responsible for developing the Galileo system,” Benedicto said. “The laboratory and testing facilities here are very much at the heart of Galileo development.”
ESA’s Receiver Testing Facility is the historic location of the first Galileo positioning fix in 2012. (Photo: ESA)
“I’m very happy to welcome members of the UN ICG group, doing a great job in bringing navigation satellite system operators together, to share achievements and challenges and encourage interoperability – our users love our systems working together.”
The tour began at ESA’s Receiver Testing Facility — historic location of the world’s very first Galileo positioning fix back in 2012 – equipped with a multitude of specialized satnav receivers for not only Galileo satellites but also the US GPS, Russian Glonass, Chinese BeiDou, India’s NAVIC and Japanese QZSS systems, together with augmentation systems such as Europe’s own European Geostationary Navigation Service, EGNOS. The signals from all these systems can also be recorded to very high fidelity for subsequent investigation or reuse.
Lab simulation systems can recreate all these outputs in combination to test receiver systems across a huge range of scenarios, such as amid interference induced by a solar storm, or to see how receivers cope while flying, or even in orbit.
Smartphone receivers can be assessed with simulated augmentation from cellular network stations, wifi mapping or inertial navigation, while simulating their user’s continuous motion. The flexibility the facility’s simulators offer also allows early testing of enhancements planned for next decade’s ‘Galileo Second Generation’ satellites.
“Our aim is to go closer to the market, and how they’re doing things because how current services are being exploited is very important for developing the next generation,” said Olivier Smeyers of ESA’s Commercial User Segment Section.
This table in ESA’s Galileo Payload Laboratory comprises a replica Galileo In-Orbit Validation satellite payload (other than its atomic clocks, which are housed separately nearby). Kept in cleanroom conditions at ESTEC in the Netherlands, it is employed for ground-based testing or anomaly investigation. (Photo: ESA)
Next came the Galileo Processing Centre, which provides ESA with continuous monitoring of Galileo services. It functions independently from the rest of the global Galileo infrastructure, to allow independent assessment of its performance, down to individual satellites and the onboard atomic clocks at the heart of the system — working closely with facilities such as the Galileo Time Validation Facility in Spain and the Galileo Control Centres in Germany and Italy.
The group was also shown ESA’s Time and Metrology Facility: an ensemble of six high-performance atomic clocks sufficiently stable to monitor the nanosecond-scale performance of Galileo System Time, and since 2012 maintaining their own timescale called UTC (ESTC), employed in turn to help set Coordinated Universal Time (UTC) — the world’s global time.
The cleanroom environment of the Galileo Payload Laboratory contains the same atomic clocks flown aboard Galileo satellites with the rest of its navigation payload, used to replicate any performance anomalies identified in orbit and make early tests of Galileo Second Generation design improvements.
“ESA is a very active member of UN ICG,” commented Rafael Lucas Rodriguez of ESA’s Galileo Services Engineering Unit and tour organizer. “We’re currently co-chairing an ICG working group on system performance enhancement and supporting the European Commission and GSA on all Galileo-related technical matters discussed at the committee.”
An aerial view of ESTEC. The Erasmus building is at front right. The T building (home to ESA’s Galileo team) is in the foreground.
By Paul Alves, Carmen Wong, Matthew Clampitt, Eric Davis and Eunju Kwak
INNOVATION INSIGHTS with Richard Langley
WE LIVE IN A POLLUTED WORLD. Sometimes even pristine environments are desecrated.
No, I’m not talking here about the rubbish on Mount Everest, nor the leaching of heavy metals from tailing ponds, nor the plastic trash in the oceans, nor the sulfur dioxide in the atmosphere.
I’m talking about radio-frequency pollution. Just as we would like to have our physical environment free of pollution for our better health and that of the ecosystem, we would like the radio spectrum to be free of pollution so that its users — virtually everyone on the planet — can have a better RF experience, whether it be when listening to the radio, using a cell phone or operating a GNSS receiver. We usually call RF pollution interference, or RFI for short, as it interferes with the signal we are trying to receive.
RFI can be accidental or deliberate, in which case we call it jamming. As a shortwave radio enthusiast, I am familiar with both types of RFI. Although the majority of the world’s radio stations attempt to coordinate their broadcasts to ensure that two stations don’t try to beam their signals to a particular area on the same or an adjacent frequency at the same time, it does happen, ruining reception. And if a country doesn’t want its citizens listening to certain foreign radio broadcasts, it might attempt to jam them as the Soviet Union did in the past and as China, North Korea, Cuba and several other countries still do.
In this month’s column, we look at GNSS interference. In many cases, GNSS interference is accidental, with a nearby radio device putting out a signal at a fundamental frequency or a harmonic, which lies within the passband of one of the GNSS frequencies.
It could be intentional, too, and we’ve all heard about GPS jammers including the so-called personal privacy devices that deliberately interfere with GPS signal reception. Is there any way to detect GNSS interference and to find its source so that remedial action can be taken? Yes and yes. A team of authors from NovAtel tell us how.
Interference is a growing concern among GNSS users, particularly in parts of the world where radio frequency transmission is not strictly regulated. Intentional interference and jamming is cheap and relatively easy to obtain in the form of personal privacy devices (PPDs). These devices can sometimes cause unintended interference and jamming to important infrastructure such as an airport. In this article, we describe a method for creating an interference map using the NovAtel OEM7 Interference Tool Kit (ITK). The ITK is capable of detecting and eliminating interference, and can be used to measure the power of a received interferer. When data is collected for an area around a static and continuously operating interference source, it can be used to map out the interference over the affected area. We overview a method for mapping the interference and, using a model of power loss over distance, creating a map of the interferer’s likely position. We also discuss simulated results and three case studies with live (real-data) interference sources from India, Canada and Japan.
NovAtel introduced the ITK in 2016. The ITK’s interference detection provides a list of sources, which includes an estimate of the frequency, bandwidth and power of the measured interference. It also provides the power levels across the entire frequency band of the front end. Either of these can be used as measurements of the received interference power levels. When the power levels for a given frequency are combined from multiple locations, they can be used to estimate the power and location of the interference source. The received power levels can also be combined to estimate the interference power as a function of location. The performance degradation experienced by one receiver at a given interference level can be extrapolated to other receivers at the estimated interference levels.
INTERFERENCE DETECTION
The ITK tools include the ability to visualize the power received across the input frequencies (front-end) bands. This can be used to quickly and easily identify any irregularities in the spectrum. These irregularities could be caused by internal interference, which is interference between electrical components introduced through hardware integration or installation. It can also be caused by external interference, such as by a PPD or other nearby radio transmitter.
The ITK’s detection feature identifies potential interference and provides a list of the interference power, frequency and bandwidth. This makes it easier for integrators to automate responses to potential interference without the need to scan the spectrum themselves. FIGURE 1 shows the received signal power and interference detection threshold for the GPS L1 frequency band. In this case there is no interference detected.
FIGURE 1. Received signal power (blue) and interference detection threshold (red) for L1.
The detection threshold is adjustable. However, if it is set too high, it can cause interference to be undetected; if it is set too low, it can cause false detection. For this example, a fairly low value was chosen because we were willing to manually identify the interference source and ignore any false detection.
The ITK also includes tools to mitigate interference, limiting or eliminating its impact. This includes a high dynamic range mode, which is effective in reducing the impact of interference. If this is not sufficient, then notch or low-pass filters also can be applied to completely cut out parts of the spectrum to neutralize the impact of interference or jamming.
FREE-SPACE LOSS
The mapping algorithm, which will be discussed later, requires a model of the power loss as a function of distance (d) to the transmitter. As the wave spreads from the transmission source, the power is lost according to:
(1)
where Lp (dB) is the power loss in dB, d is the distance in meters, and λ is the wavelength in meters. This equation can be expanded into a function of frequency (f, in Hz) and distance (d, in millimeters). Changing the units in this equation changes the constants.
(2)
For example, if the transmitter is broadcasting at 1.237 GHz, then Equation (2) gives
(3)
This ideal power loss is significantly increased by physical obstructions that are common, such as vehicles, buildings, trees or the terrain type. Different materials can have significantly different impacts on the power loss.
Some researchers have used a precomputed power map and map matching for indoor positioning. This method uses the expected received power to position a receiver. The same algorithm that is used to position the receiver could also be used to position the transmitter.
FIGURE 2 shows the received power as a function of distance that was observed for the Calgary test. There is a large variability in the power, likely due to natural obstructions.
FIGURE 2. Received power as a function of distance from the transmitter.
The equation for the line of best fit of this data is significantly different from Equation (3). This is likely due to the obstructions and limited number of data points. Due to problems with inaccuracies with this data fit, any further power calculations will use Equation (2).
MAPPING THE INTERFERENCE IMPACT
Using a single observation of the received interference power, a profile of the transmit power as a function of location can be created using a power decay curve similar to that shown in Figure 2. If we assume that the transmitter is at a given position and use the decay curve through the observed power, then we can estimate the transmit power at that location. When we do this for multiple locations, a power profile is created. This process is shown in FIGURE 3. When these plotted estimates are connected continuously, then we get a power profile.
FIGURE 3. Received power as a function of distance from the transmitter.
This power profile could pertain to a lower power transmitter that is relatively close to the receiving antenna or could be a stronger transmitter that is farther away. A single transmitter at any location could be responsible for the received power depending on the power of the transmitter.
When additional measurement points are added at different locations, the estimated powers of the transmitter for each individual observation can be combined. The estimated transmit power at some of the potential transmitter locations will match between the observations. For potential interferer locations that are far from the true transmitter location, the observations will conflict with each other.
Creating this type of power profile can be useful for pre-analysis. If we assume that none of the measurement locations can observe the interference, then the received interference must be equal to or less than the noise floor. If we assume that the received interference is at the noise floor, then we can use this profile map to identify the power of any hidden, undetectable transmitters in a region. An interferer may be broadcasting under the noise floor, undetectable at that power and distance. For example, if we want to monitor an area for interference around critical infrastructure, such as an airport, then we can deploy a network of ITK receivers. If no interference is detected, it is still possible for interference to be present if the power level of the transmitter is low enough that it does not reach any of the receivers above the noise floor. This analysis can be used to estimate the minimum detectable interference across the area, and used to determine the receiver network spacing and locations to ensure the minimum detectable interference is immediately detected.
FIGURE 4 shows an example of measurement points from the India case study. It shows the estimated power of a potentially undetectable interference source if no interference is detected anywhere at the measurement points. Lighter colors indicate a higher undetectable interference power. Notice how it is possible to miss a weak interferer that is close or a high-powered interference source that is farther away. This also illustrates how much information we can gather from zero-observation points where interference could not be detected.
FIGURE 4. Locations and power of possibly hidden interference sources that would be undetectable by observation points, shown as blue dots (Map data: Google, DigitalGlobe).
This method could be used to determine the path or spacing of receivers to monitor a region to detect interference at a certain level. With some history added into the model so that the uncertainty increased over time, a single receiver or a fleet of receivers could plan out their routes to monitor for interference.
The estimated interference source power can be used to determine the impact of the interference and give an estimate of the location of the interferer. A single static interferer will be assumed when estimating the location of the interferer using a goodness-of-fit model. A grid is created over the interference area. For each point in the grid, the attenuation (power loss) model is used to calculate the residual between the minimum transmit power and all power measurement points. If the residuals are low for all the observed power locations, then this is the most likely location of the interference transmitter.
FIGURE 5. Example of the goodness of fit for potential transmitter location and power.
FIGURE 5 shows an example of this goodness-of-fit test. The red dot shows the location of a potential transmitter location under test. Using the distance attenuation model, the predicted received power for each of the measurement points is calculated. The difference between the expected received power and the actual received power is an indication that this is not the correct transmitter location. The root-mean-square error of the fit error for all the observed points gives a likelihood that the transmitter is at this location.
SIMULATED RESULTS
Using the goodness-of-fit method, we can generate reasonable visualizations of the interference effect. FIGURE 6 shows an example map produced from simulated interference to the east.
FIGURE 6. Interference map from a simulation where the interference is on the east side (Map data: Google).
The expected power attenuation model matches perfectly with the data because it is a simulation. Similar results were obtained when the interference was assumed to come from the west and north. The yellow line shows a “roller-coaster” plot of the interference power. The height of the line shows the relative received power. Notice that it increases as we approach the source of the interference and decreases as the path moves away from the interference. A combination of the roller-coaster plot and the map give a quick visualization of the impact and location of the interference. There is a slight ambiguity between the east and west side of the road because the transmitter is close to the road. The goodness of fit works very well in this case to identify the location of the interference source.
FIGURE 7 shows a case where two interference sources are simulated. In this case, the model breaks down because it assumes that there is only a single interference source. The model clearly has difficulties determining the location of the interference. Even with accuracy issues, the model could still be used as a visualization of the interference that is easier to interpret than looking at numbers in a table.
FIGURE 7. Interference map from a simulation with 2 interference sources (Map data: Google).
INDIA DATASET
This dataset was the initial motivation for this work. A customer reported intermittent tracking problems with a newly installed receiver. The receiver would stop tracking for a few hours every evening. Customer service visited the site to investigate. Because of the intermittent nature of the problem, interference was suspected. An OEM729 receiver was walked around the affected antenna in an attempt to find the source of the interference and also to prove to the customer that interference was in fact the cause of the tracking problems.
FIGURE 8 shows the collected measurements. The numbers shown are the received interference powers at each location. It is possible to approximate the location of the interference and the impacted area by looking closely at the measurements, but it takes some close examination and interpretation.
FIGURE 8. Received interference power measured when searching for interference in India.
The source of the interference was identified using this approach. It was found to be a weather station, which performs a nightly upload of data collected throughout the day. This weather station broadcasts at 1580 MHz, which was jamming L1. The customer was able to move the interfering antenna to another site. The customer also could have used the ITK to apply a notch filter, which would have mitigated the interference’s impact, but it is better to remove the source of interference if possible.
Using the data points collected, an interference map can be generated using the method described. This map is shown in FIGURE 9. The lighter color indicates a higher likelihood that the interference transmitter is at that location. The location of the transmitter is also shown in the figure. The likelihood map is very close to the actual location of the transmitter. It gives a quick and easy-to-interpret visualization as opposed to individual measurement points.
FIGURE 9. Interference map for the India case study (Map data: Google, DigitalGlobe).
CALGARY DATASET
We were made aware of a potential unintentional L2 interference device and took it to Cross Iron Mills mall, north of Calgary, Canada, to investigate. FIGURE 10 shows a map of the area.
FIGURE 10. Map of the test area showing the location of the interference source.
We drove the path shown in blue to characterize the interference, and collected data using an OEM729 receiver with the ITK feature. Two buildings are near the interference source: a smaller building to the north and a large building to the south. These buildings block and shield the receiver from the interference when it is between the interference and the receiver.
The interference device was a transmitter to send video from a drone to a monitor, broadcasting at 1.2 GHz with 800 milliwatts. It was purchased online with no warnings about potential impacts it may have on other systems or devices. As recreational drones (and their electronics) become more popular, unintentional jammers and interference sources could become commonplace. We have no continuous monitoring and enforcement for short-range and short-duration unintentional jammers such as this one.
Although many commercial-grade receivers, such as ones common in cell phone and GPS watches, were unaffected because they only operate at L1, the box the device came in also indicates that there is a 1.5-GHz model capable of broadcasting at 2 watts. With 2 watts at 1.5 GHz, GPS L1 would be significantly jammed. This emphasizes the need for interference detection and mitigation. Nothing is stopping recreational hobbyists from accidentally jamming a significant number of users and services.
FIGURE 11 shows the roller-coaster plot of the interference observed during the test. The height of the yellow bars indicates the received power for the L2 interference. The power is generally higher closer to the interference source and decreases as a function of distance; however, there is a lot of deviation. Physical obstructions also cause significant decreases in received power.
FIGURE 11. Observed power of the interference source (yellow) over the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
For example, on the north end of the small building, shown on the right side of the figure, the observed interference power drops to almost zero despite being relatively close to the interference source. The large variations in power throughout the southern loop may be due to partial obstructions from parked cars or outcrops of the building. These physical obstructions cause larger decreases in received power than simply moving the antennas away from each other.
Since the interference was only broadcasting on L2, a position is still available through the other GNSS frequencies. The GPS receiver had difficulty tracking GPS L2 signals because of the interference.
FIGURE 12 shows the number of GPS L2 signals tracked. As the receiver approached the interference source, it became more and more difficult to track the L2 signals. As the receiver moved away from the interference, or behind a physical obstruction (like a building), the impact of the interference decreased and the signals were reacquired.
FIGURE 12. Number of L2 satellites tracked (red) over part of the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
This shows how a simple device can inadvertently be harmful. Anyone could have purchased this device to transmit video from their recreational drone. Since this device only broadcasts on L2, the GPS of the drone and many nearby devices would have been unaffected, while almost completely jamming and disrupting any dual-frequency receivers nearby.
FIGURE 13 shows the interference goodness-of-fit map from the real data test. The map shows the correct trend, but the peak of the map does not include the actual location of the interference transmitter. This is due to inaccuracies in the power attenuation model. For example, a significant shift to the south is due to the rapid decrease in power when moving behind the north building.
FIGURE 13. Interference map from the real-data test.
When only the southern dataset is considered, we get a more accurate map, one not impacted by the northern building. This is because the attenuation model does not account for obstructions. The performance of this kind of model could be significantly improved with a model that includes the topography and buildings.
Despite the inaccuracy of the map to precisely locate the interference source, these simple model maps give a nice visualization of the interference.
TOKYO REAL DATA RESULTS
We received a report of interference in Tokyo, Japan, and took a receiver there to investigate. FIGURE 14 shows the maximum received power throughout the dataset. The interference around 1570.69 MHz is obvious and easily to identify in the figure.
FIGURE 14. Spectrum power level for the Tokyo dataset.
FIGURE 15 shows the observed power of the interference source when walking around the building. There is a peak in the received power when moving to one side of the building, while the observed power is relatively constant over the other three sides of the building. This strongly suggests that the interference source is along the one side of the building.
FIGURE 15. Observed power of the interference source (yellow) for the Tokyo dataset (Map data: Google, Zenrin).
This figure also shows the estimated goodness-of-fit interference map produced using the algorithm described earlier. The source of the interference could not be conclusively determined; however, we believe that the source was emanating from one of the vehicles in the parking lot.
This real example illustrates how useful this visualization of the observed power is in understanding the nature of the interference, identifying the source and localizing its effect. The interference in this case did not cause a noticeable change in the number of satellites or signals tracked.
CONCLUSIONS
This article showed a creative and useful application of NovAtel’s Interference Tool Kit available as a feature on the OEM7 line of receivers. The ITK can be used to create maps that show the estimated location of an interferer as well as the impact of the interference on other users. We demonstrated this using simulated datasets where the agreement between the simulated and actual loss-of-power models made for overly optimistic results. Three case studies are also shown: The original motivation for this work was a customer-service case in India. The second is a case in Calgary where unintentional interference was being caused by a drone video transmitter. The third dataset from Tokyo was a similar example, where, unfortunately, the true interference source could not be conclusively identified.
The three interference case studies show the importance of interference detection and mitigation because intentional and unintentional interference sources are easy to obtain and are not easily monitored or restricted. In one of these cases, a device that was naively purchased online as a UAV video transmitter ended up jamming GPS L2 in an area of roughly 2,000 square meters. With interference mitigation, it is possible to continue to work and operate in these environments without interruption or significant impact.
ACKNOWLEDGMENTS
The authors thank Bryan Leedham and Saravanan Karuppasamy for sharing their customer stories with us and providing us with the data for the case studies. This article is based on the paper “Interference Likelihood Mapping with Case Studies” presented at ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018.
Paul Alves received a Ph.D. from the Department of Geomatics Engineering at the University of Calgary in 2006. He is a principal research engineer in the Applied Research Team at NovAtel Inc. in Calgary, Canada.
Carmen Wong is a geomatics engineer at NovAtel. She received her B.Sc. in geomatics engineering with biomedical specialization from the University of Calgary in 2008.
Matthew Clampitt graduated in 2014 with a B.Sc. in geomatics engineering from the University of Calgary and is now a developer in the Positioning Algorithms Group at NovAtel.
Eric Davis has an undergraduate degree from the University of Calgary, with majors in both astrophysics and physics. He also earned an M.Sc. in physics at the University of Calgary. He joined NovAtel in 2016.
Eunju Kwak received her Ph.D. from the Department of Geomatics Engineering, University of Calgary, in 2013. She is a geomatics engineer at NovAtel.
FURTHER READING
• Authors’ Conference Paper
“Interference Likelihood Mapping with Case Studies” by P. Alves, C. Wong, M. Clampitt, E. Davis and E. Kwak in Proceedings of ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018, pp. 467–482.
• GNSS Interference and Jamming Detection
“Interference” by T. Humphreys, Chapter 16 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
“Demonstrated Interference Detection and Mitigation with a Multi-frequency High Precision Receiver” by F. Gao and S. Kennedy in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 159–170.
“Signal Acquisition and Tracking of Chirp-Style GPS Jammers” by R.H. Mitch, M.L. Psiaki, S.P. Powell, and B.W. O’Hanlon in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 16–20, 2013, pp. 2893–2909.
• Radio Frequency Propagation Radio Frequency Propagation Made Easy by S. Faruque, SpringerBriefs in Electrical and Computer Engineering, published by Springer International Publishing AG, Cham, Switzerland, 2015.
• Localization Based on Signal Power
“Indoor Localization Based on Floor Plans and Power Maps: Non-Line of Sight to Virtual Line of Sight” by J.J. Khalifeh, Z.M. Kassas and S.S. Saab in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept. 14–18, 2015, pp. 2291–2300.
Orolia’s atomic clock solutions have been selected for the Galileo Global Navigation Satellite System (GNSS) under contracts totaling 26 million euros for an additional 12 Galileo satellites.
This latest initiative builds on Orolia’s long-standing role in providing precise timing technology for satellite programs, including Galileo.
Each satellite will carry two rubidium atomic clocks and two passive hydrogen masers, considered the most stable clock in the world. Under these contracts, Orolia will supply its Spectratime Rubidium Atomic Frequency Standard and its passive hydrogen masers physics package.
Orolia’s Space Rubidium Atomic Frequency Standard. (Photo: Orolia)
“We’re honored to continue supporting the European Commission with precise timing for Galileo,” said Orolia CEO Jean-Yves Courtois. “These new contracts further emphasize Orolia’s position as the world’s leading provider of resilient positioning, timing and navigation (PNT) solutions.”
In addition to serving as Europe’s independent PNT source, Galileo can also serve as a secondary signal source for systems such as GPS, GLONASS or BeiDou in the event of service disruption. Galileo’s quadruple clock redundancy designed into each satellite ensures that even if a failure occurs, overall system performance will not be compromised.
More than 150 Orolia Spectratime atomic clocks are flying to support Galileo, IRNSS, BeiDou, GAIA and other missions, some for more than 10 years. Orolia provides the expertise necessary to design solutions for highly reliable space applications.
Orolia is a designer and manufacturer of a full range of high-performance, low-cost GNSS synchronized crystal solutions, rubidium and maser sources, smart integrated GNSS reference clocks, rugged PNT devices, GNSS simulation and clock testing systems. Orolia’s PNT solutions support a variety of critical applications including defense, government, space, maritime, enterprise networks, aviation and telecommunications.
The University of Nottingham is working with Brazilian and European Union (EU) partners to solve atmospheric interference problems that hamper satellite-based positioning in equatorial countries like Brazil.
The research network will support the advancement of precision agriculture, which aims to make crop farming practices cheaper, greener and more efficient using satellite positioning and remote sensing.
These technologies rely on GNSS (such as GPS and Galileo) to obtain centimeter-accurate coordinates on Earth. Farmers then use this real-time precise data to optimize fertilizer use, to steer driverless machinery and for soil mapping to maximize crop production in a bid to feed a rising world population.
Despite its revolutionary potential, precision agriculture adoption rates in countries on equatorial regions such as Brazil are hindered by ionospheric scintillation in the Earth’s upper atmosphere.
Ionospheric scintillation affects the integrity, availability and accuracy of satellite positioning. Specifically, it causes interference with the propagation of satellite signals as they pass through the ionosphere, making it difficult for GNSS receivers to lock onto satellites and track their signals. This results in not only large errors but sometimes to service outages.
“The strong signal fluctuations that characterize ionospheric scintillation are caused by the irregular behavior of the ionosphere that is typical of the equatorial latitudes, affecting most of the Brazilian territory, hence the importance of the bilateral collaboration in the PEARL network,” said project leader Marcio Aquino from the Nottingham Geospatial Institute at the University.
The PEARL network, which is funded by the European Commission’s INCOBRA project, aims to tackle this problem head on to ensure high-accuracy positioning by satellite is robust and achievable in real time in Brazil.
“Solutions arising from the research will have a positive impact not only in Brazil but in the whole of Latin America, due to its geographical location near the equator and corresponding disruptive ionospheric effects,” Aquino said. “It could play a pivotal role in promoting the uptake of satellite-based positioning and the broad acceptance of the new EU system Galileo, paving the way for service implementation in other similarly affected parts of the world, such as southern China, India, Indonesia and Malaysia.”
Research and industrial partners from both Europe and Brazil will come together on the seven-month initiative to develop strategies to map the causes of ionospheric scintillation and specialized algorithms to model and mitigate their effects on satellite-based positioning.
These strategies will be part of a large Brazil-EU collaborative proposal to be submitted to the forthcoming H2020 SPACE-EGNSS call due out in October 2018.
Network members include small to medium enterprises in Europe and Brazil that are keen to incorporate new solutions that will improve their satellite-based services.
The PEARL network encompasses:
University of Nottingham, UK; Sao Paulo State University and Universidade do Estado de Mato Grosso, Brazil.
National Institute of Geophysics and Volcanology and SpacEarth Technology (an SME), Italy.
Space Research Centre of Polish Academy of Sciences, Poland.
Three small and medium-sized enterprises (SMEs): Geo++, Germany, and Alezi Teodolini and MC Engenharia Ltd, Brazil.
The European Commission funds the INCOBRA project to increase and enhance Research and Innovation cooperation activities between Brazil and the European Union. PEARL is one of INCOBRA’s bilateral R&I cooperation networks, led by the University of Nottingham, addressing one of INCOBRA’s priority areas, namely bio-economy, food security and sustainable agriculture.
According to the latest issue of the GSA GNSS market report (issue 5, 2017), revenue for GNSS device sales in precision agriculture will grow to nearly €3 billion by 2025, quadrupling from €750 million in 2013 (based on GNSS receiver sales to just this market segment).
Esri is releasing Sentinel-2 Image Services to all Esri users for no additional cost.
According to the company, Sentinel-2 is an Earth Observation Satellite that provides multi-spectral imagery for any location in the world at 10-meter resolution. Currently in beta, the service is updated daily with new imagery for all ground locations every five to seven days.
The Sentinel-2 Image Services provide temporal, multi-spectral imagery of the entire globe for improved monitoring of agricultural and forest conditions, monitoring of land cover changes, and to assist with natural disaster management.
Sentinel-2 is part of Copernicus, the world’s largest single Earth observation program directed by the European Commission in partnership with the European Space Agency.
Esri makes the multi-spectral data quickly accessible using ArcGIS Image Server and publishes an image service through the ArcGIS Living Atlas of the World, hosted on the Amazon Web Services Infrastructure.
The service includes all Sentinel-2 imagery going back 14 months, enabling change to be easily reviewed. Image analysis can be run directly on the service to create indexes displaying properties such as vegetation health or soil moisture as well as quantifying the changes over time, for better understanding of the environment.
“We are committed to helping our users discover, explore, and better understand our changing planet,” said Jack Dangermond, Esri founder and president. “Pairing Sentinel-2 imagery with our ArcGIS Image Server provides a powerful platform for in-depth analysis which can inform meaningful action.”
Sentinel-2 multi-spectral imagery can provide better visualization and understanding of catastrophic events such as Hawaii’s Kilauea volcano, the company said. The ability to use imagery of the volcano along with other spatial data, such as digital elevation models, provides an unprecedented opportunity to help predict lava flow direction and provide advanced notice to those who may be in danger.
Sentinel-2 can also help provide understanding of the conditions that lead to fires such as this past winter’s Thomas Fire, which is California’s largest wildfire on record. The Thomas Fire burned more than 280,000 acres and triggered massive mudslides. Visualizing factors, such as periods of increased moisture contributing to more lush vegetation followed by hot and dry weather, can help predict future wildfires and mitigate their effects in the future.
The Sentinel-2 imagery is available through the Living Atlas, the foremost collection of geographic information from around the globe. The Living Atlas is included with all ArcGIS online subscriptions. It is comprised of maps, apps, and data layers that support the work of thousands of Esri users around the world. Full service access, including a rolling 14-month archive of the Sentinel-2 data, is now available to all Esri ArcGIS users.
The United Kingdom and the European Union (EU) continue locked in conflict over Galileo post-Brexit, much akin to a divorce dispute over the children.
The European Commission has initiated proceedings to exclude the U.K. and its companies from security work on Galileo before the country’s exit from the bloc next year, a move that presages exclusion from use of the security features of the Galileo PRS signal.
The U.K. has responded with a demand for repayment of up to 1 billion pounds ($1.34 billion).
Both sides say they wish to continue working together on the GNSS, but the EU insists that it must be under new rules, including those preventing third countries from obtaining access to critical security information. The European Commission, executive arm of the EU, says the U.K. can no longer be trusted with sensitive data providing a secure back-up for the new satellite system.
“It’s simple: Britain is part of Galileo today as an EU member, but won’t be automatically part of Galileo tomorrow as a third-party state,” said an EU advisor. “That’s the mechanical, legal consequence of Brexit.”
The U.K. for its part has made unrestricted access a condition for a broader security collaboration.
There has been speculation that the U.K. would use the $1.34 billion alimony settlement to build a new GNSS, drawing on expertise from Australia — in effect, engendering offspring from a new marriage.
The culmination of several years of test and analysis conducted by the U.S. Department of Transportation, the assessment will play a key role in the Federal Communications Commission’s upcoming decision on a proposal from Ligado Networks.
The long-awaited Final Report for the U.S. Department of Transportation’s Adjacent Band Compatibility (ABC) Assessment was released on April 26.
The report is the culmination of several years of test and analysis conducted by the DOT, with input and assistance from the public and federal agency stakeholders. Though not explicitly motivated by it, the assessment appears to be responsive to the Positioning, Navigation, and Timing (PNT) Executive Committee’s (EXCOM’s) Jan. 13, 2012, memorandum to the National Telecommunications and Information Administration (NTIA) that sought to develop metrics to inform commercial non-space proposals for use of frequency bands adjacent to those used by GPS, so that existing and evolving space-based PNT services “vital to economic, public safety, scientific and national security needs” were not affected by implementing such proposals.
The assessment will likely play a key role in the Federal Communications Commission’s upcoming decision on a proposal from Ligado Networks to add an extensive complex of powerful ground transmitters to its system, broadcasting on frequencies allocated for satellites.
Open and Transparent. Two key attributes of the ABC assessment were that it was conducted openly and transparently, with numerous public workshops announced via the Federal Register, and it was agnostic to any particular proposal for use of bands adjacent to GPS/GNSS services. The approach chosen by DOT in performing its assessment was to develop maximum tolerable effective isotropic radiated power (EIRP) levels that could be transmitted at differing frequency offsets from the GPS L1 center frequency.
The term “adjacent” in this regard is a bit of a misnomer in that the assessment range extended to 100 MHz on either side of the GPS L1 center frequency of 1575.42 MHz. This approach was recently validated by the National PNT Systems Engineering Forum (NPEF), which found the ABC assessment was the only one of five test and analysis efforts conducted since 2011 on adjacent-band terrestrial operations that met all six of the test criteria recommended by the experts serving on the National PNT Advisory Board. The NPEF analysis is available here.
Measurements on 80 civil GNSS and GPS receivers were performed at White Sands Missile Range (WSMR) in New Mexico. The Air Force conducted a prior week of testing on military GPS receivers at WSMR, and while the results of that testing are classified, an Air Force briefing at the November 2017 PNT Advisory Board meeting indicated the military receiver test results supported the conclusions drawn by the DOT ABC assessment. Certified aviation GPS/GNSS receivers were analyzed by RTCA Inc. and are being analyzed by the FAA in terms of determining power levels in adjacent bands that don’t exceed FAA Technical Standard Orders. However, the overall ABC assessment indicates that certified aviation receivers are not the limiting case for tolerable interference from adjacent-band services to GPS and GNSS receivers.
Test Procedures
Compatibility assessment for the civil receivers consisted of conducting the initial measurements at WSMR for six categories of receivers: aviation (non-certified), cellular, general location/navigation, high-precision, timing, and space-based receivers. These were evaluated to determine what DOT called Interference Tolerance Masks (ITMs) for each category of receiver and each receiver tested. The ITMs define the maximum aggregate interfering power that can be tolerated by a given GPS or GNSS receiver. The ITMs are based on the internationally accepted Interference Protection Criterion (IPC) of a 1-dB drop-in carrier-to-noise density ratio (C/NO) for the receiver, or, equivalently, an interference density-to-noise ratio (IO/NO) of –6 dB. This 1 dB IPC standard, which NTIA directed to be used in the NPEF evaluation of the original LightSquared (now Ligado) adjacent-band proposal in late 2011, is explained in great detail in a white paper the Air Force made publicly available in 2017.
The assessment then developed, with input from the public at several workshops convened by the DOT, use cases to determine how close a receiver for a particular GPS or GNSS application might be to a base station or handset of a commercial terrestrial service in an adjacent band. Proximity distances of 10 and 100 meters were selected from these use cases, and maximum tolerable transmit EIRP levels for a given frequency offset were determined; see Figure 1. The high-precision receivers (HPRs) were the most susceptible to interference from terrestrial operations in the adjacent bands.
Figure 1. Maximum tolerable power level for GPS/GNSS receivers at 1530 MHz. (Table: DOT)
One thing that seems clear is that, with tolerable transmit power levels in the milliwatt and microwatt range, the potential to use the bands near GPS frequencies for commercial terrestrial wireless services may be limited. Illustrating that point further, the assessment shows that, based on the assumptions in the study, HPRs can be affected at distances beyond 14 kilometers (see Figure 2), and that loss of lock for low-elevation satellites can occur at distances of up to 3 kilometers from a base station providing terrestrial services using characteristics adopted internationally in the International Telecommunication Union (ITU) study groups.
Figure 2. Impact of a 29-dBW cellular base station transmitting at 1530 MHz on a high-precision GPS/GNSS receiver. (Chart: DOT)
Moreover, the assessment determined that the potential interference to other GNSS systems may be more problematic, noting that “the levels that protect all GNSS signals can be as much as 15 dB lower than those needed to protect L1 C/A signals from base station emissions with an average difference of 3.5 dB across all frequencies and five categories considered.”
Galileo’s Role. Since 2013, according to a Public Notice from the FCC, the European Commission has sought a waiver of FCC rules that require licensing of receivers operating with foreign satellites so that Galileo service can be provided in the United States. The FCC has yet to act on this waiver request, which was issued in a January 2017 Public Notice, despite overwhelming public support and a positive recommendation from the Executive Branch in 2015.
Figure 3. Bounding masks for each category corresponding to the 10 MHz LTE interference signal and L1 C/A GPS signal: general aviation, general location and navigation, high precision, timing, space-based, cellular. (Graph: DOT)
Conclusions
It is well known that all receivers take in some power from signals transmitted in nearby frequency bands. Considering this fact, the ABC assessment is relatively unique in that it examines the overall spectral environment in which GPS/GNSS operations can be affected rather than just the band allocated to the Radionavigation-Satellite Service (RNSS, the broad radiocommunication service defined in the ITU and in domestic rules under which GPS and other GNSS systems operate) between 1559–1610 MHz. That the overall environment should be considered is an important aspect of any discussion of protecting GPS and other GNSS services given the U.S. National Space Policy that was signed into effect June 28, 2010, that directs the U.S. government to “take necessary measures to sustain the radiofrequency environment in which critical U.S. space systems operate.” This policy is still in effect, and it would be difficult to argue that GPS is not a critical U.S. space system.
Recently, the reconstituted National Space Council adopted four recommendations, one of which related to spectrum used for satellite services and said that NTIA should coordinate with the FCC to ensure “the protection and stewardship of radio frequency spectrum necessary for commercial space activities.” Stewardship that is consistent with National Space Policy would include sustaining the RF environment for GPS.
As the PNT EXCOM has made clear, GPS is “vital to economic, public safety, scientific, and national security needs” of the U.S. Moreover, economic analysis presented to the PNT Advisory Board in 2015 estimated the economic benefit to the nation of GPS services at over 68 billion dollars annually. With the release of the ABC assessment, definitive information is now available to inform decisions on use of frequencies near those used to provide space-based PNT services so these critical services are not disrupted or degraded.
TerraGo, a provider of dissemination and collaboration software for defense and intelligence agencies, has announced the availability of R3 for immediate download in the National Geospatial-Intelligence Agency’s (NGA) GEOINT App Store.
R3 is a mobile data collection and collaboration app customized for the missions of reconnaissance, response and recovery. Designed for the most challenging missions and environments, R3 lets users keep working offline and off the grid with customizable workflows for security, humanitarian and disaster relief programs, the company said.
“R3 enhances situational awareness, search and rescue, damage assessments and recovery efforts,” said Scott Lee, director of federal programs at TerraGo. “It really gives users the best of both worlds with mobile technology that can go anywhere, and will also work even when the network doesn’t.”
Image: TerraGo
Designed with a simplified user interface, R3 provides a robust standalone capability for a variety of field-based collection activities. Users can access custom basemaps from numerous GIS, map and imagery sources including GeoPDF, ArcGIS, USGS and GXP, while collecting and exchanging location-tagged notes using smart forms, photos, videos and audio files.
Pre-loaded forms are available for structured assessments, and users can connect to a secure server to create unique mission packages and enable synchronous collaboration. R3 supports important standards like OGC GeoPackage interoperability and sharing geospatial assets among mission partners.
Registered GEOINT App Store users can download TerraGo R3 for iOS here. The Android version is complete and coming soon, the company added.
The three-year program helps emerging business partners bring new and innovative products to Esri customers.
The initial partnership between Hangar and Esri will enable ArcGIS customers to request and receive autonomous, precision-captured drone data on demand from within ArcGIS, enabling industries to gain real-time awareness and insight about locations and features.
The GIS community has grown accustomed to ambiguous and infrequent imagery. While emerging robotic enablers like drones provide a high-resolution, low-cost alternative to satellite and manned aircraft imagery, there hasn’t been a feasible way for GIS professionals to repeatedly gather precision location insight at scale, from potentially thousands of features within Esri maps, Hangar said in a statement.
Hangar not only makes aerial data possible at this scale, but also available on request from within ArcGIS. Using a system of systems, Hangar streamlines and automates the 4D data supply chain, enabling task-and-receive reality capture. In the near future, ArcGIS users will be able to request aerial insights at any feature, and have imagery delivered back in 24 to 48 hours or less.
“The pain we see in the GIS community is an inability to quickly and efficiently pair 2D data with the 3D reality,” said Jeff DeCoux, CEO and founder of Hangar. “We’re excited to work with Esri to deliver on-demand, precision 4D insight to ArcGIS users. Hangar will enable businesses to take full advantage of robotics as instruments of data collection, and provide the industry much needed repeatability and scale.”
ArcGIS Online users will have the capability to request and receive aerial imagery at variable frequencies or volumes. Requests can be made manually, on an as-needed basis, or automatically, based on contextual triggers or volume requirements. Data is autonomously captured, automatically processed, then delivered back to the customer via a high-speed delivery engine.
The digital missions behind requests are saved indefinitely, and can be performed repetitively with absolute precision and accuracy, preserving data integrity over time. ArcGIS users will be able to view captures within 24 to 48 hours from the initial request, across a variety of data types.
“Hangar empowers Esri users to explore any of the thousands of features within ArcGIS maps, observing ground truth at each pin in incredible detail, today and over time,” said Francis Kelly, Esri, global partner programs manager. “Hangar adds valuable data validity and scalability to the budding drone industry. We’re excited to work with them to give Esri users the ability to analyze and consume physical world content in a new and meaningful way.”
As big-data levels of precision spatial data are collected over time, Hangar will work with Esri to intelligently apply change detection and pattern recognition to enable a new era GIS that includes artificial intelligence and machine learning.
Hangar will be attending the Esri User Conference, July 9-13 in San Diego, at booth Z19 to demonstrate its technology and showcase the partnership with Esri.
Hexagon’s Positioning Intelligence division has released the PIM7500 GNSS receiver explicitly designed for autonomous automotive platform development and solutions.
The single-sided receiver features a compact form factor that solders down directly for easy integration with electronic control modules and artificial intelligence (AI) development platforms, the company said.
The new receiver features dual-frequency GNSS reception from all available constellations including GPS, GLONASS, Galileo, BeiDou, NavIC, QZSS and SBAS. It offers sub-meter and centimeter-level positioning using Hexagon Correction Services to deliver the high-accuracy positioning required for the autonomous industry.
The PIM7500 is available in low to mid-volume quantities, making it a suitable GNSS receiver for mileage accumulation fleets.
“Hexagon Positioning Intelligence has a strong commitment to the automotive market and will utilize its leadership in GNSS-based technology to provide high precision and safe positioning systems to the automotive market — now and in the future,” said Andreas Niemann, business development manager at Hexagon Positioning Intelligence.
PIM7500 chosen for autonomous buses
Autonomous commuter buses are being developed by Bertrandt, with the PIM7600 GNSS receiver. The test system will be installed on a bus in Regensburg, Germany. (Photo: Patrick Reinig)
Bertrandt, a European company that specializes in automotive controls technology development, has selected the PIM7500 receiver as the precise positioning component on its innovation platform.
Bertrandt’s innovation platform uses the PIM7500 receiver and inertial measurement unit (IMU) from Hexagon Positioning Intelligence, combined with lidar sensors, to perform image processing for object detection, collect precise route data and generate highly accurate maps.
The innovation platform will be implemented on one of the public transportation electric busses in Regensburg, Germany.
“We are pleased to have Hexagon Positioning Intelligence onboard our innovation platform for this project,” said Ulrich Haboeck, team leader of electronics and software development at Bertrandt. “Hexagon Positioning Intelligence is the perfect fit to provide the GNSS sensor components for the platform because their technology will ensure the success of the project.”
Bertrandt announced the innovation platform on May 16. Hexagon Positioning Intelligence will be participating in Bertrandt’s TechDays Sept. 27-28 to demonstrate automotive and safety-critical GNSS and inertial solutions.
“Bertrandt is an ideal technology partner for us, and we are excited to be invited to have the PIM7500 as a component on their innovation platform,” Niemann said.
Hexagon’s Positioning Intelligence division has achieved a milestone toward its goal of safe autonomy on the road. The division is developing functionally safe positioning technologies for fully autonomous vehicles and other applications.
A third-party audit has been completed that confirms process compliance with key automotive specifications ISO/TS 16949 and ISO 26262 Functional Safety Design Assurance. This is an important step toward the development of functionally safe new technology that meets the exceptional safety standards set by the automotive industry, Hexagon said.
“We’re thrilled to have our core engineering processes updated to meet the requirements of automotive applications,” said Jonathan Auld, vice president of Safety Critical Systems, Hexagon’s Positioning Intelligence division. “We are building on a 25+ year history in safety of life solutions for the marine and aviation industries, and we expect this leadership to serve us well in automotive.”
Honeywell has released new smart airport technology that is designed to enhance the safety and efficiency of airside operations.
The Honeywell NAVITAS software suite intelligently integrates air and ground traffic control with maintenance operations so airports can more easily accommodate growing air traffic while promoting safety and on-time performance, the company said.
NAVITAS was developed to comply with the latest industry standards, including those from the International Civil Aviation Organization (ICAO), European Aviation Safety Agency (EASA), International Electrotechnical Commission (IEC), Federal Aviation Administration (FAA) and European Organisation for Civil Aviation Equipment (EUROCAE).
NAVITAS includes modular and scalable software components, combined with an intuitive user-friendly interface, providing real-time insights for air traffic controllers and maintenance operators.
The components assist personnel in visualizing and routing aircraft movements despite the increasing complexity and stress associated with today’s airport operations. NAVITAS can enhance situational awareness about traffic conditions, more safely expedite aircraft turnaround times, and automate fault diagnostics for airside equipment, among other features.
NAVITAS modules include Tower Manager, Engineering Manager, Surface Manager and Performance Manager.
Tower Manager helps improve the productivity of air traffic controllers by enhancing situational awareness of airport surface operations. It gives controllers access to rich, real-time information on ground, air traffic and meteorological conditions, presenting the information in a single easy-to-use interface, and providing visibility into a multitude of traffic events while reducing the chance for error.
The system helps manage the air traffic controller’s responsibilities, while making it easier to issue and track aircraft clearance information to keep landings and takeoffs safer and on time.
Surface Manager helps airports get more out of their existing infrastructure by enhancing ground traffic safety, fluency and throughput in a wide range of weather conditions, while helping to reduce their fuel burn and carbon footprint.
The module also provides the software necessary for airports to use all four levels of an Advanced-Surface Movement Guidance and Control System (A-SMGCS), including surveillance, routing, guidance and airport safety support, along with enhanced movement conflict detection and resolution, which can include “follow-the greens”-based guidance that automatically illuminates lighting on the tarmac to guide aircraft to clear taxiways.
Engineering Manager helps engineers and technicians manage maintenance by enabling them to more effectively monitor system health, more easily perform fault diagnostics and to streamline workflows, which also often helps reduce operational costs.
It features a mobile interface and automates the diagnostics and failure reporting, while simplifying manual tasks and reducing paperwork, giving personnel better visibility into the availability, reliability and performance of airside systems. Personnel can easily create, manage and issue work orders to expeditiously resolve issues and keep equipment up and running.
Performance Manager features dashboards that allow airport staff to collaborate and analyze operations in line with key performance indicators. The module — accessible both on premise and remotely — provides access to a common base of holistic information and allows for the application of big-data analytics for real-time and predictive insights, often enabling more efficient and informed decision-making.
“Airports around the globe are seeing dramatic increases in traffic, and while that makes the world more connected, it increases complexity to ensure safe and reliable operations,” said Sonja Strand, vice president and general manager for Honeywell’s Global Airports Business. “NAVITAS helps orchestrate these complex environments like never before through mobile applications, dashboards and heads-up displays that are intuitive. By harnessing the power of the internet of things, we’re making data user friendly, and making airports smarter in the process.”
The initial partnership between Hangar and Esri will enable ArcGIS customers to request and receive autonomous, precision-captured drone data on demand from within ArcGIS, enabling industries to gain real-time awareness and insight about locations and features.
