Hexagon | NovAtel has released its latest firmware for the OEM7 family of GNSS receivers, featuring improvements in positioning reliability and accuracy.
Proven in tough defense markets, and now available for all customers, the firmware release sets new standards for innovation and reliability with the introduction of advanced tracking capabilities that enhance GNSS performance in challenging or obstructed environments, such as under foliage or in mixed urban scenarios.
Customers can now benefit from enhanced position accuracy and availability, leveraging NovAtel’s SPAN GNSS+INS technology, precise point positioning (PPP), and real-time kinematic (RTK) capabilities. This ensures greater operational availability and reduced downtime, according to the company.
Key features
Tracking improvement of 5-7 decibels (dB) and acquisition improvement of 4-5 dB for most GNSS signals
PPP root mean squared (RMS) error improvement up to 26% under challenging conditions
RTK RMS error improvement up to 15% under challenging conditions
Up to 48% improvement on the 3D position error when using SPAN in severely GNSS-challenged environments.
“Our latest firmware release is a testament to our dedication to innovation and customer success,” said Jonathan Auld, president, NovAtel Positioning Division, Hexagon. “By enabling receivers to track weaker signals and maintain positioning in the most challenging environments, we are empowering our customers with the tools they need to overcome obstacles and achieve their goals with confidence.”
The latest firmware for OEM7 GNSS receivers is now available. Visit NovAtel to download it for your specific platform.
Hexagon│NovAtel’s OEM7 GNSS receivers have successfully tracked Xona Space Systems PULSAR signals generated by a simulator from Spirent Communications. This test proved NovAtel GNSS receivers can track a Spirent simulated L-band signal identical to the PULSAR signal broadcast by Xona’s low-Earth orbit (LEO) satellites.
The Xona LEO signals will complement GNSS, improving resiliency, security, and precision for positioning, navigation and timing (PNT).
“Using Spirent’s simulated PULSAR signal, we have successfully tested our receiver’s capability to track the L-band signal planned to be broadcast from Xona’s LEO satellites,” Sandy Kennedy, VP of innovation at Hexagon’s Autonomy and Positioning division, said. “The OEM7 is a powerful platform, designed for both resiliency and flexibility; it is exciting to test our forethought by trialing this new signal type.”
The driver, developed by NovAtel engineers, provides an optimized interface enabling users to accelerate autonomous development projects by quickly incorporating NovAtel OEM7 receivers into custom applications.
The driver is available for immediate download through the new NovAtel GitHub repository or as a ROS Binary Package for direct installation.
With the release of a NovAtel-developed OEM7 driver built on ROS, developers can now confidently access the critical data needed to build autonomy algorithms for academic investigations, ride-share programs, and other applications.
Data from numerous sensors can be combined to help move projects into higher levels of autonomy faster without the need to adapt community-developed drivers. Tested using the Hexagon | AutonomouStuff platform, the driver ensures that the data received accurately reflects the output provided by the receiver, while also giving users the ability to record raw data for post-processing.
“We are excited to introduce our first purpose-built driver powered by ROS to the GitHub community. Its development is a result of collaboration between NovAtel and AutonomouStuff in support of Hexagon’s Smart Autonomous Mobility (SAM) initiative, unveiled at CES 2020 in Las Vegas,” said Miguel Amor, chief marketing officer, Hexagon’s Autonomy & Positioning division. “The SAM portfolio is a comprehensive solutions platform that brings together all the necessary sensors, software and services to make autonomous driving possible.”
The new driver is available for download on the NovAtel GitHub repository.
NovAtel, part of Hexagon’s Positioning Intelligence division, now brings users greatly improved processing speed and accuracy as well as significantly reduced signal acquisition time through the latest 7.07.03 firmware release.
The SPAN CPT7. (Photo: NovAtel)
The firmware works best with the recently launched TerraStar-X correction service, which delivers accuracy and reliability, as well as the OEM7, SPAN CPT7 and PwrPak7 products, which use signals from all GNSS constellations and frequencies to provide users with reliable autonomy and exceptional positioning availability.
The 7.07.03 firmware offers a significant improvement to the SPAN GNSS + INS (inertial navigation system) technology. SPAN with 7.07.03 shows improvements of up to 20% in the horizontal position over the entire SPAN IMU catalog and across various industry use cases including agriculture and marine. SPAN with 7.07.03 also provides improved motion detection, resulting in more robust time to convergence.
“The 7.07.03 firmware features improvements to both our SPAN Marine and SPAN Rail profiles that will greatly impact application performance and consistency,” noted NovAtel Director of Product Management, Neil Gerein, “The SPAN Marine Profile sees improvements to the heave performance and will allow users to start their work significantly faster thanks to a simplified setup for applications in marine dynamics. The SPAN Rail Profile improves position accuracy over long GNSS outages, which is crucial for applications in rail environments that often deal with potential signal obstructions such as trees, tunnels and dense urban areas.”
To download the 7.07.03 firmware update for your platform, click here.
The NavIC Indian constellation is now supported in NovAtel’s latest firmware release for its OEM7 series of GNSS receivers.
The 7.05.04 firmware release for OEM7 provides the following benefits:
Users can achieve a single point position accuracy of 2.5 m (rms) using NavIC L5 signals (from the Indian Regional Navigation Satellite System) with GPS L1 on the newly available JSN model.
Access to the L5 frequencies on the OEM7600 and OEM7720 provides triple-frequency capabilities to unlock the potential of GPS L5, Galileo E5a and AltBOC, Beidou B2a and NavIC L5.
A full listing of all changes in this release are included in the “What’s New” document included in the firmware download package. Visit this page to download the latest firmware for a specific platform. Visit NovAtel’s documentation portal for the OEM7 reference manual.
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.”
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 NovAtel OEM6 GNSS receiver card used in the CEESCOPE echo sounder has been replaced with NovAtel’s latest low-power, high-performance OEM729 receiver.
With 555 channels, the new GNSS option brings a vast increase in available channels for future-proofing, improved interference rejection and better performance in challenging environments, the company said.
The TerraStar L-Band support remains.
The OEM729-equipped CEESCOPE is available with a built-in UHF radio modem and direct Ethernet connectivity to the GNSS receiver for NTRIP cell-phone real-time kinematic corrections.
NovAtel’s OEM7 v7.03.00 firmware is now available on all OEM7 receivers. The OEM7700, OEM719 and OEM729 can be updated to the 7.03.00 firmware, which supports new features like the SPAN Land Vehicle technology, direct inertial measurement unit (IMU) connections and tracking of the NavIC Indian regional satellite system on the L5 frequency.
The SPAN Land Vehicle feature provides performance benefits specifically for extended loss of GPS signals, robust alignment routines and improved attitude performance for fixed-wheel land vehicle applications. During extended periods of GNSS outage, typically in low-dynamic operating environments or in dense urban canyons, the SPAN Land Vehicle feature optimizes integrated GNSS+INS performance to maintain accurate position, velocity and attitude.
To achieve this, NovAtel uses intelligent vehicle dynamics modeling and its patented Antenna Phase Windup Technology. Intelligent vehicle modeling identifies IMU errors in the integrated GNSS+INS system that accumulate after extended GNSS outages and reduces the impact of those errors within the SPAN solution.
NovAtel’s Antenna Phase Windup technology is used to sense changes in direction and, when combined with intelligent vehicle modeling, corrects for IMU errors in attitude (roll, pitch, yaw).
Users can now connect SPAN enabled OEM7 receivers directly to the ADIS-16488 and Epson G320N IMUs using an SPI interface, and to the STIM300 IMU using RS422, without the need for an interface card.
OEM7 receivers with NavIC L5 frequency tracking enabled will be able to access the test signals of the Indian Regional Navigation Satellite System (IRNSS) before it becomes operational (targeted for early 2018).
