Ceva has launched the successor to its Ceva-Dragonfly NB1 solution targeting the NB-internet of things (IoT) market, the Ceva-Dragonfly NB2.
The Dragonfly NB2 is a highly integrated and modular solution optimized for Cat-NB2 (3GPP Release 14 eNB-IoT) that can seamlessly be incorporated into chips and modules by the multitude of companies looking to address the large and fast-growing cellular IoT space.
GNSS hardware package. For customers developing NB-IoT products that also require GNSS capabilities, Ceva-Dragonfly NB2 includes a new power-optimized GNSS hardware package, with GNSS RF receiver and multi-constellation digital front-end.
The GNSS package speeds up both acquisition and tracking tasks by up to 8 times compared to Ceva-Dragonfly NB1, enabling a host of popular NB-IoT use cases, including people, livestock and asset tracking, and geo-fencing, the company said.
IoT boom forecast. In the latest edition of the Ericsson Mobility Report, the forecast for cellular IoT increased significantly, almost doubling to 3.5 billion connections for 2023. The report cites large-scale deployments in China and increasing interest in eNB-IoT and Cat-M1 cellular IoT standards as the catalysts for 30 percent CAGR between 2017 and 2023.
Ceva-Dragonfly NB2 is a licensable Rel14 compliant eNB-IoT solution and builds on the success of Ceva-Dragonfly NB1, which has been widely licensed for a range of use cases and emerging end markets, including smart cities, transport and logistics and consumer electronics. It is centered on the Ceva-X1 DSP/control processor featuring an enhanced Instruction Set Architecture and provides a unified processor environment for both physical layer and protocol stack workloads.
The solution also includes a highly integrated, worldwide enabled RF transceiver, a power amplifier (PA) and all the associated hardware and software modules required to develop a complete eNB-IoT product, ensuring the lowest possible bill-of-materials (BOM) in the process.
In addition to the performance improvements enabled by Release 14 including higher data rates and lower latency, Ceva-Dragonfly NB2 features a range of enhancements to ensure higher performance, added functionality and increased security for NB-IoT applications compared to its predecessor.
A new power management solution, complete with intelligent sleep mechanisms ensures ultra-low sleep power consumption of a few microAmps, further improving the battery life critical to every NB-IoT device.
The enhanced RF design is already silicon-proven at 55nm and 40nm processes, further lowering the entry barriers for customers with no previous cellular expertise to enter this burgeoning market.
Ceva-Dragonfly NB2 also includes the fully optimized physical layer and protocol stack firmware designed for Release 14 Cat-NB2. The addition of an on-chip embedded flash memory and controller now allows full NB-IoT design on a single die which further reduces BOM and power consumption.
Voice trigger. Ceva-Dragonfly NB2 also supports use cases requiring always-listening voice trigger, voice commands and sound sensing. The flexibility of the Ceva-X1 IoT processor allows for these sensing features to be implemented in software. The Ceva ClearVox voice front-end software package, for example, can be used to ensure clear and intelligible voice pickup for use cases such as emergency calls and voice panic buttons. In terms of security, Ceva-Dragonfly NB2 integrates a completely redesigned secure platform, including smart interfaces to connect USIM or eSIM. Ceva also offers other complementary technologies addressing massive IoT, such as Bluetooth 5 dual-mode and low energy and Wi-Fi 802.11n/ac/ax, for short range connectivity which customers can leverage for their product designs.
“The widespread commercial deployment of NB-IoT is well underway across the globe and we’re proud to be at the forefront of technology innovation for long-range massive IoT,” said Michael Boukaya, vice president and general manager of the wireless business unit at Ceva. “With the introduction of Ceva-Dragonfly NB2, we have built on the considerable success we achieved with our first generation solution, and delivered a unique, silicon-proven eNB-IoT Release 14 solution for our customers that is unprecedented in terms of system completeness, performance and power efficiency.
“Moreover, the option of power-optimized GNSS, voice and sensing capabilities vastly increases the breadth of use cases our customers can address with this licensable solution,” Boukaya said. “There is no other IP company in the world today that can come close to offering such a complete solution for eNB-IoT and we’re excited to closely partner with our customers to create a whole new wave of applications and devices for the infinite Internet of Things.”
Ceva-Dragonfly NB2 is available for licensing now. Development kits and reference silicon will be available in the third quarter of this year.
Telit, a global enable of the internet of things (IoT), has debuted its SE878Kx-A series of GPS and GNSS integrated antenna receiver modules for consumer and business applications. According to the company, these modules provide high performance, maximum reliability and low power consumption.
In addition, the SE878K3-A and SE878K7-A are compatible with GPS, GLONASS, Beidou and Galileo and also enable device vendors to develop quickly and cost-effectively location-based IoT solutions for use in virtually any country worldwide, Telit said.
The SE878Kx-A series supports dual internal-external antennas to ensure connectivity when one is broken or compromised, along with a SAW filter to maximize jamming immunity. According to Telit, these features make the modules ideal for mission-critical applications and other use cases where reliability is key, such as alarms, stolen cars or high-end asset tracking.
The SE878Kx-A series also provides seamless integration with Telit’s cellular modules, including eCall/ERA-GLONASS compliant solutions.
“The new SE878Kx-A series is the latest example of Telit’s leadership in providing GNSS solutions for applications that demand the highest reliability and performance,” said Yossi Moscovitz, Telit president of products and solutions. “Just as important, the modules give IoT designers maximum flexibility, faster development cycles, easier integration and the ability to develop once and deploy worldwide.”
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.
Hexagon’s Positioning Intelligence division has released the PIM7500 GNSS receiver explicitly designed for autonomous automotive platform development and solutions.
The single-sided receiver features a compact form factor that solders down directly for easy integration with electronic control modules and artificial intelligence (AI) development platforms, the company said.
The new receiver features dual-frequency GNSS reception from all available constellations including GPS, GLONASS, Galileo, BeiDou, NavIC, QZSS and SBAS. It offers sub-meter and centimeter-level positioning using Hexagon Correction Services to deliver the high-accuracy positioning required for the autonomous industry.
The PIM7500 is available in low to mid-volume quantities, making it a suitable GNSS receiver for mileage accumulation fleets.
“Hexagon Positioning Intelligence has a strong commitment to the automotive market and will utilize its leadership in GNSS-based technology to provide high precision and safe positioning systems to the automotive market — now and in the future,” said Andreas Niemann, business development manager at Hexagon Positioning Intelligence.
PIM7500 chosen for autonomous buses
Autonomous commuter buses are being developed by Bertrandt, with the PIM7600 GNSS receiver. The test system will be installed on a bus in Regensburg, Germany. (Photo: Patrick Reinig)
Bertrandt, a European company that specializes in automotive controls technology development, has selected the PIM7500 receiver as the precise positioning component on its innovation platform.
Bertrandt’s innovation platform uses the PIM7500 receiver and inertial measurement unit (IMU) from Hexagon Positioning Intelligence, combined with lidar sensors, to perform image processing for object detection, collect precise route data and generate highly accurate maps.
The innovation platform will be implemented on one of the public transportation electric busses in Regensburg, Germany.
“We are pleased to have Hexagon Positioning Intelligence onboard our innovation platform for this project,” said Ulrich Haboeck, team leader of electronics and software development at Bertrandt. “Hexagon Positioning Intelligence is the perfect fit to provide the GNSS sensor components for the platform because their technology will ensure the success of the project.”
Bertrandt announced the innovation platform on May 16. Hexagon Positioning Intelligence will be participating in Bertrandt’s TechDays Sept. 27-28 to demonstrate automotive and safety-critical GNSS and inertial solutions.
“Bertrandt is an ideal technology partner for us, and we are excited to be invited to have the PIM7500 as a component on their innovation platform,” Niemann said.
PCTEL Inc. has launched a new series of multi-GNSS L1/L2/L5 antennas for precision navigation and timing.
According to the company, the antennas combine aerospace-level precision with global satellite compatibility, in a highly durable package. They enable critical applications including vehicular automation, 5G network timing synchronization and Positive Train Control (PTC) systems.
The company made the announcement at the RSSI C&S Exhibition (Railway Systems Suppliers Inc.) being held this week in Omaha, Nebraska.
PCTEL’s multi-GNSS L1/L2/L5 antennas increase the accuracy of timing and location information by providing simultaneous access to multiple GNSS signals across multiple frequency bands. The antennas support all relevant GPS, GLONASS, BeiDou and Galileo frequencies with excellent multipath mitigation and high out-of-band rejection for greater signal clarity, the company said. Their robust AAR and IP67-compliant design makes them suitable for years of use on railways and in other harsh real-world environments.
“Precision navigation is crucial for the next generation of autonomous vehicle technologies, which could drive major improvements in safety and efficiency across a wide variety of industries,” said Rishi Bharadwaj, senior vice president and general manager of PCTEL’s Connected Solutions group. “PCTEL’s new antennas make precision navigation accessible for large-scale deployments in rail, public safety, agricultural and commercial fleets. They also enable commercial deployments of 5G networks, which have higher accuracy requirements for network timing.”
Earlier this month, PCTEL released its 900-MHz MIMO Yagi antennas with dual polarization., designed for use with MIMO or diversity radios in advanced supervisory control and data acquisition (SCADA) systems and other industrial internet of things applications. PCTEL’s dual polarized antenna technology improves data throughput and reliability on both licensed and unlicensed spectrum.
PCTEL is displaying its new multi-GNSS antennas along with other antenna solutions for the rail industry, May 22-23 at the RSSI C&S Exhibition, booth #1109. The new antennas will be available for purchase in mid-July.
Honeywell has released new smart airport technology that is designed to enhance the safety and efficiency of airside operations.
The Honeywell NAVITAS software suite intelligently integrates air and ground traffic control with maintenance operations so airports can more easily accommodate growing air traffic while promoting safety and on-time performance, the company said.
NAVITAS was developed to comply with the latest industry standards, including those from the International Civil Aviation Organization (ICAO), European Aviation Safety Agency (EASA), International Electrotechnical Commission (IEC), Federal Aviation Administration (FAA) and European Organisation for Civil Aviation Equipment (EUROCAE).
NAVITAS includes modular and scalable software components, combined with an intuitive user-friendly interface, providing real-time insights for air traffic controllers and maintenance operators.
The components assist personnel in visualizing and routing aircraft movements despite the increasing complexity and stress associated with today’s airport operations. NAVITAS can enhance situational awareness about traffic conditions, more safely expedite aircraft turnaround times, and automate fault diagnostics for airside equipment, among other features.
NAVITAS modules include Tower Manager, Engineering Manager, Surface Manager and Performance Manager.
Tower Manager helps improve the productivity of air traffic controllers by enhancing situational awareness of airport surface operations. It gives controllers access to rich, real-time information on ground, air traffic and meteorological conditions, presenting the information in a single easy-to-use interface, and providing visibility into a multitude of traffic events while reducing the chance for error.
The system helps manage the air traffic controller’s responsibilities, while making it easier to issue and track aircraft clearance information to keep landings and takeoffs safer and on time.
Surface Manager helps airports get more out of their existing infrastructure by enhancing ground traffic safety, fluency and throughput in a wide range of weather conditions, while helping to reduce their fuel burn and carbon footprint.
The module also provides the software necessary for airports to use all four levels of an Advanced-Surface Movement Guidance and Control System (A-SMGCS), including surveillance, routing, guidance and airport safety support, along with enhanced movement conflict detection and resolution, which can include “follow-the greens”-based guidance that automatically illuminates lighting on the tarmac to guide aircraft to clear taxiways.
Engineering Manager helps engineers and technicians manage maintenance by enabling them to more effectively monitor system health, more easily perform fault diagnostics and to streamline workflows, which also often helps reduce operational costs.
It features a mobile interface and automates the diagnostics and failure reporting, while simplifying manual tasks and reducing paperwork, giving personnel better visibility into the availability, reliability and performance of airside systems. Personnel can easily create, manage and issue work orders to expeditiously resolve issues and keep equipment up and running.
Performance Manager features dashboards that allow airport staff to collaborate and analyze operations in line with key performance indicators. The module — accessible both on premise and remotely — provides access to a common base of holistic information and allows for the application of big-data analytics for real-time and predictive insights, often enabling more efficient and informed decision-making.
“Airports around the globe are seeing dramatic increases in traffic, and while that makes the world more connected, it increases complexity to ensure safe and reliable operations,” said Sonja Strand, vice president and general manager for Honeywell’s Global Airports Business. “NAVITAS helps orchestrate these complex environments like never before through mobile applications, dashboards and heads-up displays that are intuitive. By harnessing the power of the internet of things, we’re making data user friendly, and making airports smarter in the process.”
MicroPilot Inc. has teamed with Trimble to integrate high-precision GNSS technology as part of its autopilot for guidance and control of unmanned aerial vehicles (UAVs).
With centimeter-level, real-time kinematic (RTK) positioning capabilities, Trimble’s multi-constellation GNSS receivers are capable of tracking signals from GPS, GLONASS, Galileo and BeiDou, the company said. Trimble GNSS receivers are used in a wide variety of applications ranging from port automation and robotics to autonomous vehicle guidance.
MicroPilot develops and manufactures autopilots for UAVs, including the triple-redundant MP21283X. The company also provides support products that enable customers to use their development time as efficiently as possible and bring their products to market faster. These products include the trueHWIL2 UAV autopilot simulator and the XTENDERmp software development kit.
The MP21283X UAV autopilot. (Image: Micropilot)
Working closely with Trimble gives MicroPilot the ability to better leverage Trimble’s GNSS technologies. This access improves the ability of MicroPilot’s support team to assist customers with their product development, testing and operations. Trimble will benefit from MicroPilot’s extensive experience integrating guidance, navigation and control systems for a wide variety of UAV platforms, the companies said.
“Reliable, robust and innovative GNSS solutions as well as strong technical support is key to bringing any UAV to market and our relationship with Trimble will allow MicroPilot to improve on our already industry-leading support,” said MicroPilot president Howard Loewen.
“We are very pleased to be working closely with MicroPilot to provide high-precision GNSS for its UAV autopilot solutions,” said Joseph Carey, director of strategic initiatives for Trimble’s Integrated Technologies Division. “MicroPilot autopilot’s simple installation, configuration and customization capabilities allow UAV manufacturers to easily integrate reliable, state-of-the-art, professional guidance, navigation and controls to their aerial platforms.”
Sony Corporation has developed two new products, the Spresence main and extension boards for internet of things (IoT) applications, equipped with a smart-sensing processor.
The main board uses a multi-CPU structure equipped with Sony’s GNSS receiver (GPS+GLONASS) and high-res audio codec. A variety of systems for diverse applications — drones, smart speakers, sensing cameras and other IoT devices — can be built by combining the boards and developing the relevant applications.
Technological information about the products’ software and hardware is publicly available via open platform, allowing for a wide range of developmental possibilities and further expanding the market.
Positioning information and audio input/output functions are expected to become increasingly important in the expanding IoT market. The main board operates on low power and features a smart-sensing processor, with a built-in GNSS receiver and an audio codec that supports high-resolution audio sources. It employs a hexa-CPU, multi-core configuration that makes it easy for anyone to create high-performance, highly versatile applications.
For example, the new board can be used to control a drone using GPS positioning technology and a high-performance processor, voice-controlled smart speakers, low-power consumption sensing cameras and other IoT devices. It can also be combined with various sensors for use in systems that detect errors in production lines on the factory floor.
The IoT boards will be displayed at the Maker Faire Bay Area 2018 starting May 18 in San Mateo, California, and on Aug. 4-5 at the Maker Faire Tokyo 2018 in Tokyo, Japan.
STMicroelectronics is offering new high-stability MEMS sensors for the Industrial Internet of Things (IIoT).
The new sensors, to be made available sometime this year, begin with the IIS3DHHC, a 3-axis accelerometer optimized for high measurement resolution and stability to ensure accuracy over time and temperature.
The IIS3DHHC targets precision inclinometers in antenna-positioning mechanisms for communication systems, Structural Health Monitoring (SHM) equipment for keeping buildings and bridges safe, and stabilizers or levelers for a wide variety of industrial platforms.
Its long-term accuracy and robustness are also suitable for high-sensitivity tilt and security sensors, as well as image stabilization in high-end digital still cameras (DSCs), the company said.
STMicroelectronics also provides what it calls “product longevity” to assure long-term availability of components in industrial equipment.
The IIS3DHHC has a full scale of ±2.5 g and is capable of providing the measured accelerations to the application through an SPI 4-wire digital interface.
The sensing element is manufactured using a dedicated micromachining process developed by STMicroelectronics to produce inertial sensors and actuators on silicon wafers.
The IC interface is manufactured using a CMOS process that allows a high level of integration to design a dedicated circuit which is trimmed to better match the characteristics of the sensing element.
The IIS3DHHC is available in a high-performance (low-stress) ceramic cavity land grid array (CC LGA) package and can operate within a temperature range of -40 degrees Celsius to +85 degrees Celsius.
Key Features of the IIS3DHHC
Included in the 10-year longevity program
3-axis, ±2.5 g full-scale
Ultra-low noise performance: 45 µg/√Hz
Excellent stability over temperature (<0.4 mg/°C) and time
16-bit data output
SPI 4-wire digital output interface
Embedded FIFO (depth 32 levels)
Embedded temperature sensor
12-bit temperature data output
High shock survivability
Extended operating temperature range (-40 °C to +85 °C)
ECOPACK, RoHS and “Green” compliant
“These high-quality industrial sensors leverage our investments in MEMS design and high-yield fabrication processes to deliver superior performance with low ownership costs for applications where the highest precision, repeatability and robustness are critical,” said Andrea Onetti, group VP and general manager, MEMS Sensors Division, STMicroelectronics. “We will continue to introduce new types of precision sensors for industrial applications in the coming months, covered by our 10-year longevity commitment, including combination sensors, specialized sensors and complete inertial modules.”
The IIS3DHHC is in production now, in a high-quality 16-lead 5 mm x 5 mm x 1.7 mm ceramic LGA package, priced from $4.50 for orders of 1000 pieces.
Qianxun Spatial Intelligence Inc., a high-precision positioning service provider, and u-blox are joining forces to deliver high-precision positioning solutions to the Chinese market.
By coordinating their product offerings, they seek to meet growing demand for increased positioning accuracy for mass-market applications. Some of the areas driving up demand for high-precision positioning services in China are internet of things (IoT) tracking devices such as those used on shared bikes, as well as automotive, UAV and robotic vehicle applications.
u‑blox is bringing to the partnership its high-precision GNSS receivers. Its u‑blox F9 multi-band positioning platform uses integrated real-time kinematic (RTK) technology to process the high-precision positioning correction data provided by Qianxun SI, delivering down to centimeter-level positioning accuracy for wide-ranging applications. It enables even faster and more robust performance by leveraging a greater variety of GNSS signals.
Two major advancements have enabled sub-meter-level positioning accuracy for mass-market applications. The first is modern GNSS correction services that constantly monitor GNSS signals to determine positioning errors caused, for example, by atmospheric distortions, and wirelessly transmit correction data to compensate for these errors to millions of GNSS devices. The second is a new generation of small, power-efficient, and affordable GNSS receivers that are able to use the correction data to achieve such high levels of accuracy.
Qianxun SI, a high-precision positioning service provider, has already laid the groundwork for the large-scale expansion of high-precision positioning in the IoT era, the company said. Based on BeiDou, which is compatible with GPS, GLONASS and Galileo, Qianxun SI’s high-precision positioning service is built on the nationwide ONE Network, composed of more than 2,000 Continuously Operating Reference Stations (CORS) and using proprietary algorithms. It offers vehicles and other applications a range of 24/7 high-precision positioning services in most regions of the country.
By the end of 2018, Qianxun SI’s dynamic centimeter-level service will cover the entire mainland of China, the company said.
“We are delighted to cooperate with u-blox to provide users with high-precision positioning solutions that are user friendly and affordable,” said Jinpei Chen, CEO of Qianxun SI. “I believe our high-precision positioning technology is a key enabler of IoT development, and the cooperation with u‑blox will accelerate the go-to-market process of the technology in an extensive range of industrial and automotive market applications.”.
“This collaboration is a genuine win-win for all involved in that it allows us to develop high-precision solutions that will foster innovation across markets,” said Thomas Seiler, CEO of u-blox. “Partnering with China’s leading GNSS correction service provider allows u-blox customers to bring cutting edge applications to the China market in the shortest possible time.”
Swift Navigation has issued a new firmware upgrade to its flagship product Piksi Multi GNSS module.
This marks the fifth major point release to Piksi Multi and is available free of charge to Swift customers. The most recent provided GLONASS support, among other features.
The firmware release also enhances Duro, the ruggedized version of the Piksi Multi receiver housed in a military-grade, weatherproof enclosure designed for long-term outdoor deployments.
Duro – Piksi enclosure.
Firmware Release 1.5 for Piksi Multi and Duro supports four regional Satellite Based Augmentation Systems (SBAS) — the United States-based Wide Area Augmentation Systems (WAAS), the pan-European Union-based European Geostationary Navigation Overlay Navigation System (EGNOS), the Japanese Multifunctional Transport Satellites (MTSAT) Satellite Augmentation System (MSAS) providing coverage for Japan and Australia and the GPS-Aided GEO Augmented Navigation (GAGAN) regional system operated by the Indian government.
These four regional satellite systems are used to improve the overall performance of GNSS such as GPS and GLONASS, both of which are supported by Swift’s receivers.
SBAS support is particularly relevant for Swift customers located in places where cell phone coverage is sparse or is not available, such as rural areas where precision agriculture operations are taking place or alternatively in marine locations, lakes, in-land waterways and up to approximately 100 miles off shore where cellular or internet coverage may not be feasible.
Applications using SBAS do not require a local reference station, allowing rovers such as drones, combines and other agricultural equipment and marine vessels to benefit from satellite corrections accurate to a sub-meter, when centimeter-accuracy is not required and where internet or cell coverage is spotty or absent.
SBAS Support — The new firmware adds support for WAAS + EGNOS + MSAS + GAGAN regional satellite constellations and augments standard positioning performance for GLONASS (G1/G2) + GPS (L1/L2C) for use with Swift Navigation products.
Acquisition Improvements — Firmware 1.5 allows Piksi Multi and Duro a faster time to first fix and once a signal has been acquired, improves accuracy and availability. Time to first RTK fix was improved by 21 seconds.
Standard Positioning Performance (SPP) Enhancements — Time to first SPP improved by 7 seconds.
Increased Satellite Count for RTK — Increased satellite count used in the RTK engine improves RTK performance in all environments, particularly those where skyview is partially obscured and/or rapidly changing.
“The addition of four regional satellite constellations for our devices enhances reliability and improved position accuracy in challenging or remote environments where autonomous vehicles may have limited or no cell coverage. Essentially, SBAS provides a free corrections service, allowing our precision agriculture, marine and other customers to receive satellite corrections without a base station,” said Anthony Cole, Ph.D., director of the measurement and positioning team at Swift Navigation. “Being hardware-ready means that Piksi Multi and Duro users simply download the 1.5 firmware at no additional cost, to get the latest features and performance improvements.”
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.
