NovAtel Inc. has launched its TerraStar-C PRO correction service with multi-constellation support, including the GPS, GLONASS, Galileo and BeiDou constellations.
Combined with NovAtel’s OEM7 positioning technology, TerraStar-C PRO cuts initial convergence times by nearly 60 percent and offers 40 percent better horizontal accuracy than the current TerraStar-C service, the company said.
NovAtel’s TerraStar-C PRO offers a robust multi-constellation solution that provides greater positioning accuracy, availability and reliability than before, the company added. With the growing number of operational GNSS satellites, TerraStar-C PRO offers benefits in challenging signal conditions such as multipath, shading, interference and scintillation. High-rate TerraStar-C PRO corrections provide reconvergence in less than 60 seconds following brief GNSS signal interruptions.
According to NovAtel, TerraStar-C PRO corrections are generated using TerraStar’s proprietary global network of more than 100 strategically located GNSS reference stations. The correction data is delivered worldwide through overlapping geostationary satellites directly to a NovAtel receiver or via cellular IP network.
With OEM7 triple L-band support, TerraStar-C PRO correction signals from up to three satellites can be tracked and used simultaneously, providing continuous correction data reception when the primary satellite signal is blocked.
“TerraStar-C PRO enables higher operational efficiency by allowing users to start operations sooner and continue to work through challenging conditions without interruptions,” said Sara Masterson, NovAtel’s positioning services segment manager. “We continue to build our TerraStar portfolio of services and with the addition of TerraStar-C PRO customers can trust that they have not only a highly-reliable precise positioning solution, but also services that immediately translate to increased productivity.”
TerraStar-C PRO is available immediately as a termed subscription service for agriculture, unmanned, airborne and land applications, such as survey, mapping and GIS and supported on compatible OEM7 products with firmware version 7.05 and later.
The airport’s new autonomous ATV begins testing in August. (Photo: Edmonton International Airport)
An autonomous all-terrain vehicle (ATV) equipped with NovAtel Inc. technology will soon join the security fleet at the Edmonton International Airport in Alberta, Canada.
The ATV will be used to detect people and animals that breach the airport perimeter, as well as locate holes in the fence to alert the security team.
This is the only known autonomous ATV to be used for airport security and it will be used to monitor its 20-kilometer fence line on a narrow perimeter road, according to Hexagon, NovAtel’s parent company.
The unarmed vehicle is controlled remotely by humans and can also drive autonomously, incorporating machine-learning to perform its tasks.
The vehicle system includes navigation, path planning, obstacle avoidance, animal and human recognition, communication systems to airport security, geo-fencing, and situational awareness and analysis.
The autonomous ATV patrols will focus on the following:
Identifying damage to the chain-link fence and fence posts, verifying barbed wire is taut and undamaged, and detecting holes or gaps under the fence
Detecting human or animal activity
Searching for obstacles using lidar
“We would not have been able to navigate the vehicle on such a narrow road if we had not used NovAtel gear,” said Ken Brizel, CEO, ACAMP.
The autonomous security ATV was developed by the Alberta Centre for Advanced MNT (microprocessor and nanotechnology) Products (ACAMP).
The airport is a member of the Advanced Systems for Transportation Consortium established by ACAMP and supported by the Government of Alberta. ACAMP is a member of the Alberta Aerospace and Technology Centre at EIA. ACAMP and EIA were able to harness technologies developed by consortium members to construct and test the autonomous ATV security vehicle, readying it for regular use at EIA.
A LinkedIn post from the company suggests theater-goers “Keep an eye on the scene where Tom Cruise flies a helicopter through a narrow canyon to catch our Compact GNSS Antenna in action!”
Above is a behind-the-scenes video that shows how Airbus helped Cruise, an actor known for doing many of his own stunts, learn to fly a helicopter and navigate a treacherous dive sequence (with the aid of the NovAtel receiver).
NovAtel is now delivering its SPAN tightly coupled GNSS+INS navigation technology in a rugged, ultra-compact unit.
Commercially exportable and designed for integration into a wide variety of applications, the high-performance SPAN CPT7 delivers assured positioning anywhere, in a package one-quarter the size of the company’s SPAN-CPT.
SPAN technology leverages generations of precise positioning expertise and advanced algorithms to tightly couple GNSS and inertial navigation system (INS) measurements. The system enables continuous, robust positioning and fast reacquisition in challenging navigation environments where GNSS signals may be unreliable or unavailable for short periods.
The new SPAN CPT7 also incorporates dual antennas to deliver instant alignment, along with interference detection and mitigation using NovAtel’s OEM7 Interference Toolkit (ITK) technology.
Integrators can take advantage of a spectrum analysis function of ITK to identify interference within the GNSS frequency bands and whether interference is coming from the external environment or due to other components in an integration project, NovAtel said. Mitigation features within ITK allow developers to implement digital filters and eliminate the problem.
Combining the multi-frequency, multi-constellation technology of the OEM7720 receiver board with ITK and the high-performing micro-electromechanical systems (MEMS) IMU, the SPAN CPT7 delivers anti-jamming functionality in an ultra-compact enclosure that fits in the palm of your hand. When paired with the GPS Anti-Jam Antenna (GAJT) in military applications, SPAN CPT7 is an integral part of assured position, navigation and timing (A-PNT).
“The remarkable new SPAN CPT7 delivers solutions for defense, mobile mapping, and autonomous vehicle applications with more flexibility than ever before,” said Neil Gerein, director, product management, NovAtel. “This new product saves customers space and weight without compromising accuracy or performance.”
NovAtel’s Natasha Wong Ken offers a rundown on the company’s latest products — including its PwrPak7D enclosure, OEM7600 GNSS receiver and Inertial Explorer Xpress software — at Xponential 2018, which took place April 30-May 4 in Denver.
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.
AUVSI Xponential was a big show once again — 8,500 attendees, more than 600 exhibitors, 200 educational sessions and 400 speakers. The show floor was huge as usual, with virtually every kind of UAS product and service imaginable for inspection at small, large and larger booths or display areas.
The morning kick-off presentation on Tuesday was enthusiastic about the coming large-scale adoption of drones and associated robotic technology, with a couple of real-time examples — driverless vehicles at Babcock Ranch in Florida and drone supply deliveries for humanitarian aid in Rwanda.
A view of show floor.
However, there still remain a number of barriers to wide-scale integration of drones into daily life from a regulation perspective, as Steven Bradbury, general counsel of the U.S. Department of Transportation, pointed out — while at the same time also indicating that the Federal Aviation Administration (FAA) has granted hundreds of waivers where the safety case has been adequate for lots of commercial UAS operations.
Most of the major GNSS players were exhibiting at the show, so we focused on gathering their news while also collecting a flavor of the many drone system suppliers in attendance.
U-blox introduced its new ZED-F9P multi-band, multi-constellation chip — with GPS, GLONASS, Galileo and BeiDou signal reception and processing and on-chip multi-band RTK with fast convergence times — promising centimeter-level accuracy and low 85 mA (4x GNSS) power consumption in a 17 mm x 22 mm package.
ZED-F9P signals: coverage added in two stages. Option A – available now. Option B – available Q2/2020.
Initial urban testing in Finland in challenging conditions has indicated RTK performance at 9 cm 94%, with high availability, short convergence times (<10 seconds) and fast reconvergence. This kind of performance is apparently initially targeted at automotive applications — u-blox is a member of the Sapcorda automotive group — and is forecasting samples for this July, with production beginning before the end of this year.
The NovAtel tagline for the show was “Assured PNT,” which matches many U.S. and International agency objectives — this was accompanied by several announcements for both commercial and government agency products and applications.
The integrated E1 package includes NovAtel’s SPAN technology, which optimizes positioning and attitude performance during extended GNSS outages. Both new PwrPak enclosures come with the Interference Toolkit advanced interference detection and mitigation capability.
With most UAVs, the electronics on the airframe can produce a disruptive internal interference environment, and can lead to potential problems for the integration of sensitive GNSS. To help overcome this issue, NovAtel has released the OEM7600 receiver board in an extremely small form factor, enclosed with protective shielding to reduce the effects of emissions from nearby electronics.
The 7600 comes with 555 channels, multi-frequency/constellation positioning; L-band support for TerraStar corrections; serial, USB, CAN and Ethernet interfaces; advanced interference detection and mitigation features; RTK; GLIDE and Steadyline firmware options with 20-g vibration rating and the option to add integrated SPAN GNSS + inertial.
NovAtel also announced Inertial Explorer Express, which provides the same core processing and utilities as Waypoint Inertial Explorer software for applications including unmanned aerial vehicles (UAVs) and smaller projects. Inertial Explorer Express will produce centimeter-level position and attitude solutions for lidar, camera and other sensor data with faster processing times and reduced complexity.
On the government/agency business side of the house, NovAtel has been quite successful with the GAJT antenna, which includes integrated anti-jam technology. GAJT is in use operationally and has been shipped to 16 allied nations around the globe, with the latest success being with the artillery Observation Post Vehicles (OPV) for the Canadian Army.
The NovAtel GAJT-710ML GPS anti-jam antenna.
The Type 26 Frigate of the British Fleet will use NovAtel anti-jam technology. (Photo: BAE Systems)
Canadian OPVs are used on the front-line of combat, so its essential that their location and timing information should not be compromised by enemy jammers. The NovAtel GAJT is readily retrofitted to existing vehicles to provide the necessary jamming defense needed by front-line forces.
Previously, NovAtel also announced the selection of GAJT for the UK fleet of Type 26 Frigates – providing essential anti-jam protection for its onboard navigation system.
MB-Two module by Trimble.
Chris Wheeler and Omar Subra were good hosts when we visited them at the Trimble booth — Chris first made a YouTube video for GPS World (see below) and then gave me some insights into what’s new.
Basically, the OEM line has rolled over new versions of almost all individual receiver boards, with the addition of the BeiDou B3 frequency, capability for RTX PPP (precise point positioning) corrections, the addition of new constellations and inertial integration options.
An updated MB-Two receiver module can be configured for single frequency GPS through to dual frequency GPS, GLONASS, QZSS, Galileo and Beidou, uses RTX PPP and has an improved RTK engine for cm positioning from a base-station, or from over-the-air RTK corrections, or provides relative RTK against a moving base.
A typical Trimble application could include capturing an Insitu ScanEagle UAV in a difficult shipboard multipath environment with integrated GNSS-inertial, UAV navigation and control, UAV payload stabilization, or providing a “truth-system” for autonomous unmanned ground vehicles.
Since last year when Trimble introduced a “cell-phone” software receiver application, one useful application could have involved an insurance company using a “pocket-carried” antenna (with integrated RF) for field incident assessments. The cell-phone software license would be transferable to other assessors in the department, while a few pocket antennas are available for the whole assessment crew. This saves purchasing a whole load of hardware, and being limited to where the functionality can be moved or deployed. Everyone has a cell phone, and the relatively inexpensive antenna/RF can be available to all needing them.
Watch this video to learn about Trimble’s latest products, including its BD990 and BD992 GNSS receiver boards.
Trimble is also ramping up its OEM customer service and repair capabilities to improve turn around for multiple customers and applications in the field. Improved results are beginning to help customers and its OEM business, while increased R&D investment is expected to put new products into the field in the fall.
This year Intel’s emphasis continued to be on how to manage the huge amount of data that high-precision visual and multi-spectral cameras are gathering by UAVs carrying out asset inspections for their customers. The Intel view is that this data is useless to an end-user unless it is interpreted and presented in a format that can be readily understood and used for the purpose it was intended.
Let’s say a company operates 75 drones inspecting installations it owns or operates across several states, and that 50 GB of data is the nominal amount of data each drone collects on each mission. That means that nearly 4 TB of data could be collected daily if all 75 drones operate at once. More likely, over 1 TB daily shows up in a central location — a huge amount of unprocessed data.
In a live demonstration, Intel showed how a typical installation inspection — by a drone taking high-resolution still photos at a remote location – could be collected and managed. Once in an Intel processing environment, the data quickly became visual format in 2D or 3D, and could be accessed remotely by an inspection team, saving significant travel costs and time to actionable results.
Intel also promised to soon exceed its record at the Pyeongchang Winter Olympics for the number of drones flown at once — currently set at 1,218 drones. The company’s next target is for a light display using 1,500 multi-colored drones.
Insitu CEO Esina Alic
Insitu held a media event at the show to announce its ScanEagle-3 drone system. Esina Alic, the new Insitu CEO, led a team who introduced and then unveiled the new commercial-standard ScanEagle variant.
This new variant has grown out of 20 years of experience and 15 years of working with the FAA to enable integration of drones into the U.S. National Airspace System (NAS). The ScanEagle-3 (SE-3) has been rebuilt with the objective of developing a certifiable vehicle with increased payload and endurance capability that is free of any ITAR restrictions — allowing export without restrictions to the rest of the world.
Insitu unveils the ScanEagle-3 is at Xponential 2018.
SE-3 features include:
Significantly increased (x2) payload
Still provides for full integration of all existing payloads
Commercial, non-ITAR product for the global market
Long-endurance platform
Service contracts available
Product release in Q2 2019
Fully compatible with existing launch and recovery systems
Around ~100 lb without payloads
ScanEagle variants were used in emergency response to the California wildfires at Santa Rosa and Medicina, gathering real-time information for fire-line combatants.
ScanEagle helped fight these wildfires using High Accuracy Photogrammetry (HAP) sensors. Military-grade electro-optical (EO) cameras during daylight and infrared (IR) cameras for night-time imaging extended the time available for tracking fire lines. Penetrating smoke or darkness, these UAVs gathered video and still images that were used to create geo-referenced, high-resolution digital fire progression and suppression maps to guide firefighting on the ground.
This small overview of Xponential 2018 attempts to provide a flavor of the breadth of activity we saw at the show last week. A good portion of this has also been captured through short videos published on the GPS World website, along with news articles.
There is more to come, with a report to follow from the show on Septentrio’s new product releases, Spirent’s GNSS simulation demo, DJI’s overview of drone products featured at the show, CyPhy Works tethered drones, Swift’s announcement of its Skylark correction service trials, Hemisphere’s new Vector Smart Heading Antenna, and Harxon’santennas for drones.
A big show to cover, that’s for sure! It’s a good sign that people were perhaps talking more business than in previous years and a sign that the UAS industry is perhaps moving into its next growth phase.
The PwrPak7D-E1 is a robust, high-precision receiver that has multi-frequency, dual-antenna inputs and provides GNSS multi-constellation heading and position data. These capabilities make the PwrPak7D-E1 suitable for ground vehicle, marine or aircraft-based systems. NovAtel’s Synchronous Position, Attitude and Navigation (SPAN) technology brings together GNSS positioning and inertial navigation to provide an exceptional 3D navigation solution that is stable and continuously available. The PwrPak7D-E1 has a powerful OEM7 GNSS engine, integrated Epson G320N micro electromechanical (MEMS) inertial measurement unit (IMU), built-in Wi-Fi and 16 GB of internal storage.
NovAtel has demonstrated high-accuracy positioning performance using automotive-grade GNSS chipsets Teseo APP and Teseo V from STMicroelectronics. Combining automotive-grade multi-frequency GNSS chipsets with positioning algorithms and correction services from NovAtel improves the achievable positioning accuracy available to automotive users and provides a solution suitable for autonomous operation.
According to the company, these chipsets provide multi-frequency GNSS data for precise point positioning (PPP) and real-time kinematic (RTK) to enable accurate positioning capabilities. Teseo APP features built-in integrity checking for use in safety-critical systems, whereas Teseo V is used for non-safety-critical precise positioning applications.
The collaboration between the two companies is designed to reach car manufacturers and Tier 1 suppliers for future production models.
Test results: Horizontal position errors. Teseo V alone is shown in red, Teseo V + NovAtel in green. Test results: Horizontal cumulative error distribution. Teseo V alone is shown in red, Teseo V + NovAtel in green. (Chart: NovAtel)
Test results: Horizontal cumulative error distribution. Teseo V alone is shown in red, Teseo V + NovAtel in green. (Chart: NovAtel)
Driven Today. “STMicro is one of many chipset manufacturers coming to market with dual-frequency chipsets targeting the automotive sector,” said Jonathan Auld, VP Engineering and Safety Critical Systems for NovAtel. “We are taking advantage of their expertise in automotive measurement engines for high-volume, cost-effective reliable positioning. NovAtel brings high-precision algorithm expertise and integration with global corrections supplied by Hexagon Correction Services to this initiative.”
NovAtel’s positioning engine combines the GNSS measurements from these chipsets with inertial measurement unit (IMU) data and Hexagon Correction Services to deliver centimeter-level PPP positioning solutions in real time.
“Working closely with STMicroelectronics allowed us to innovate and drastically reduce time to market of our assured positioning solution tailored specifically for safe positioning of autonomous vehicles,” added Auld.
Comparison of GNSS Performance possible in automotive today (red), L1 automotive with corrections (green) and L1/L2 automotive with corrections (blue).
Driverless Tomorrow. “Precise absolute positioning is just one piece of the overall autonomous vehicle puzzle and must be done with safety and integrity concepts in mind.” Auld pointed to the partnership announced in 2016 between NovAtel, the Illinois Institute of Technology, and Stanford University to conduct leading-edge research to determine how GNSS technology can deliver a positioning solution that meets both the safety and accuracy requirements of autonomous automotive vehicles.
Previous research by academia and industry into GNSS integrity produced the successful WAAS program for aviation. The new work underway will extend the scope to include the autonomous ground vehicle use case. The research includes updated and expanded concepts for high-integrity carrier-phase algorithms as well as expanded threat models and safety monitors.
At the Automotive Tech.AD in Berlin, Auld added: “Today the primary use case for positioning in navigation is single-frequency GNSS, with up to 2 constellations, using narrowband RF and antennas, obtaining accuracy at the 1–2 meter level. This is primarily done with pseudorange-based positioning techniques, with some carrier-phase assistance. There are no functional safety standards, and so safety data is provided on the output solution.”
Autonomous Requirements. By contrast, he continued, autonomous operation will require lane-level and better accuracy: 3D centimeter to decimeter absolute positioning. This means multi-frequency, multi-constellation receivers and antennas to improve overall accuracy and increase available measurements. It will also require increased availability through sensor fusion with IMUs and other sensors. All of this must be brought together through a functionally safe development process targeted at ISO26262 Automotive Safety Integrity Level (ASIL) B.
Moving from meter to centimeter level position requires additional processing to handle all the added signals coming in; residual monitoring and observation exclusion, and carrier phase, “the key to centimeter-level positioning,” as opposed to code phase. The vehicle’s localization system must include enhanced positioning algorithms for multipath mitigation, a fast converging corrections network, enhanced Kalman Filters, and sophisticated sensor fusion.
Flexible Integration. NovAtel’s positioning engine architecture enables a flexible integration with different GNSS receiver chipsets, augmentation sensors and processor environments, providing automotive manufacturers with additional flexibility when it comes to sourcing of components and subsystems of advanced driver assistance systems (ADAS) and autonomous driving solutions.
The positioning engine is being developed to ASIL-B standards and will include a proprietary GNSS integrity solution to ensure safe positioning within defined protection limits tailored to the customer’s application requirements.
NovAtel has introduced several new precision positioning solutions for space-constrained applications. With enhanced positioning accuracy in a compact form, the PwrPak7D, PwrPak7DE1 and OEM7600 are suitable for automotive, airborne and other smaller unmanned systems.
PwrPak7D and PwrPak7D-E1 are dual-antenna, multi-frequency enclosures, and the OEM7600 receiver board, plus NovAtel’s new Waypoint Inertial Explorer Express post-processing software are being showcased this week at AUVSI Xponential 2018.
Dual-Antenna, Multi-Frequency Enclosures
The new PwrPak7D enclosure. (Photo: NovAtel)
NovAtel’s new PwrPak7D and PwrPak7D-E1 enclosures provide space efficiency without sacrificing position accuracy and heading stability, even in stationary, slow-moving or hovering dynamics.
The PwrPak7D-E1 enclosure integrates an inertial measurement unit (IMU) with NovAtel’s OEM7720 dual-antenna receiver board to deliver GNSS and inertial navigation system (INS) capabilities.
When combined with NovAtel’s SPAN technology, positioning and attitude performance is optimized during extended GNSS outages.
Both the PwrPak7D and PwrPak7D-E1 include NovAtel’s Interference Toolkit with advanced interference detection
and mitigation features applicable to all stages of integration. A web user interface, accessible through Ethernet or
Wi-Fi, allows for quick and easy system configuration and control.
OEM7600 Receiver Board for Smaller Autonomous Systems
The OEM7600 receiver board. (Photo: NovAtel)
The OEM7600 receiver board features NovAtel’s high-performance positioning solutions in an extremely small form factor, wrapped with protective shielding to isolate emissions from surrounding electronics in confined spaces.
This new receiver integrates easily with NovAtel’s SPAN technology to optimize performance during extended GNSS outages.
The new OEM7600 will be commercially available this summer.
New Post-Processing Software for UAVs and Small Project Areas
Inertial Explorer Xpress centroid circle. (Image: NovAtel)
Inertial Explorer Express provides the same core processing and utilities as the
Waypoint Inertial Explorer software for applications including unmanned aerial vehicles (UAVs) and smaller projects.
Inertial Explorer Express will produce centimeter-level position and attitude solutions compatible for lidar, camera and other sensor data with faster processing times and reduced complexity
“We are very excited to be introducing our new OEM7-based and Inertial Explorer solutions at Xponential 2018,” said Neil Gerein, director of product management at NovAtel. “These systems provide robust positioning and accuracy in a compact footprint for UAVs and smaller autonomous projects. An advanced range of software options, including NovAtel’s tightly coupled GNSS+Inertial SPAN technology and Interference Toolkit, provide assured positioning anywhere.”
Phoenix Lidar’s Scout System features NovAtel SPAN GNSS/IMU equipment and a pinwheel antenna. Combined with Phoenix’s hardware and software, this lightweight UAV lidar system serves in agriculture, construction and other general mapping applications. Here the Scout is integrated with the DJI M600 Pro UAV. (Photo: Phoenix Lidar Systems)
As a UAV flies, it is subject to roll, pitch and yaw movements, adversely affecting the high-definition imagery that industrial-grade UAVs are designed to collect. Three measures combat unwanted movement: a stabilizing gimbal, a high-quality GPS/inertial measurement unit (IMU) integration, and orthorectification of the data during post-processing.
Imaging applications are driving all sectors of the booming UAV market. The increasing availability and variety of compact, robust, lightweight sensors, employing a range of super-resolution and often multi-spectral and hyperspectral technologies, continuously expand and improve UAV applications.
Three companies exhibiting at the Association for Unmanned Vehicle Systems International’s (AUVSI’s) massive Xponential show May 1-3 will showcase recent advances in this arena.
Challenges of Airborne Imaging. Size and weight govern UAV deployment.Imaging sensors must fit compact payload bays. An integrated UAV solution will typically include an imaging sensor, a high-performance GPS/inertial measurement unit (IMU), and a data storage hub to collate streams of data from all connected instruments.
Software geared specifically to flight supplies image orthorectification and manages sensor operation during the mission, enabling users to input GPS coordinates for sensor operation. Outside of defined coordinates, the sensor will not collect data, reducing the amount of data to store or transmit.
Immediate or real-time processing and georeferencing of imaging products has always been key to defense and security applications; it becomes critical for precision agriculture, cartography, civil engineering, remote monitoring and surveillance, intelligent inspection, disaster preparedness and risk study, newsgathering, cinematography, tourism and even commercial advertising. A multisensor landscape view can improve a UAV’s ability to react intelligently without operator input.
Integrated GPS/INS exhibitors at the Xponential show include:
NovAtel (Booth 3219). The company uses a flexible technology platform and diverse OEM products, which include SPAN technology: tightly coupled GNSS receivers with IMUs for reliable, continuously available, position, velocity and attitude, to deliver its vision of assured positioning — anywhere.
NovAtel offers TerraStar Correction Services to provide accurate real-time sub-meter or decimeter positioning around the world, anytime. Its Waypoint Inertial Explorer Xpress post-processing software provides the same core processing and utilities as Inertial Explorer along with simplified functions and workflows tailored for UAV markets and small project areas.
VectorNav (Booth 2214). Engineers at Octopus ISR integrated the VectorNav VN-200 GPS/INS directly into the optical bench of a gimbal to deliver positioning accuracy under flight conditions such as high vibrations, accelerations and temperature fluctuations. The device flies aboard the UAV Factory’s miniature Epsilon series of gyro-stabilized gimbals, enabling the Precision Geo-Lock feature, which combines a GPS-aided inertial navigation system with dedicated software algorithms and payload operator software.
The VN-200 features 16g accelerometers and 2000°/sec gyros in a postage-stamp-sized surface-mount device and a rugged package. Epsilon gyro-stabilized turrets are available with both VectorNav’s VN-200 single GPS-based INS solution and the VN-300 dual GPS-based INS.
SBG Systems (Booth 2535). The company developed specific calibration procedures to provide reliable heading even when UAVs tilt. Magnetometer calibration can be processed in 2D on the ground, or in 3D in flight. Qinertia software enhances inertial navigation systems performance by post processing inertial data with raw GNSS observables.
SBG Systems’ Ellipse 2 Micro high-performance inertial sensors reduces size and costs and for volume projects. It is available as an inertial measurement unit (IMU), or as an attitude and heading reference system (AHRS) or inertial navigation system (INS) running an extended Kalman filter, connected to an external GNSS receiver.
A: The availability and quality of GPS in the application is critical. Industrial-grade MEMS IMUs can provide survey-grade performance when high-quality GPS is continuously available, but even tactical-grade MEMS cannot provide more than a couple of minutes of GPS-denied navigation. The level of integration between the two technologies is also important. Even comparing two systems using the same sensors, the performance is highly dependent on the ability of the system designer to leverage their respective strengths.
A: Successful integration of inertial sensors with GNSS requires understanding both the goals and environment of the application. Consider the required accuracy of attitude and position, severity of GNSS obstructions, expected dynamics and environmental conditions. Tradeoffs in size, power and cost narrow the choices, but achieving the desired performance is more nuanced. Data sheets for IMUs can also be notoriously difficult to compare. My advice is to focus on the goals and listen to the experts.
A: There is a clear need for reliable consumer-grade GNSS/INS in GNSS-degraded environments. In this case, two key aspects are: removal of measurement outliers, mostly caused by multipath; and adequate modeling of inertial errors. The first aspect is efficiently addressed via residual monitoring, especially with GNSS carrier phase. A 15-state INS error model is generally sufficient. Yet, modeling parameters and contribution of other terms such as axis misalignment must be evaluated using test data.
