Category: Transportation

Road navigation using multiple dissimilar environmental features

Look Around

Road Navigation Using Multiple Dissimilar Environmental Features

New navigation paradigms combining GNSS and inertial with additional sensors can increase overall reliability and power robust road navigation. A feasibility study tests a barometer, a magnetometer and a camera looking at road signs, and concludes that such sensors examining environmental features can supply the necessary context for frequently traveled or shared routes.

By Debbie Walter, Paul D. Groves, Bob Mason, Joe Harrison, Joe Woodward and Paul Wright

Where a robust and reliable position solution is required, it is necessary to combine GNSS with other technologies. Dead-reckoning is only suitable for bridging short outages. For robustness against longer GNSS outages, alternative position fixing techniques are needed. Radio-based signals have been excluded from this study as they are either not yet mature or are, like GNSS, susceptible to jamming, though they may still play a part in the final navigation solution.

For land navigation in particular, a new approach is therefore needed. Environmental features provide a potential source of location information. These include buildings or parts thereof, signs, roads, rivers, terrain height, sounds, smells and even variations in the magnetic and gravitational fields. Visual navigation technologies are being developed and are likely to be complementary to the feature-matching discussed in this article; however, they will not be directly discussed. The environmental features will be integrated with dead reckoning to provide robust positioning.

The overall solution is to place hardware within a batch of vehicles, comprising multiple sensors, including a GNSS receiver and sensors for dead reckoning. Road map matching could also be included. During normal usage, the GNSS receiver is used for positioning and a database is updated with the feature information from all the sensors accompanied by location stamps from the GNSS-based position solution.

As the multiple vehicles travel around an area, the database is built up for these routes. In the event that the GNSS receiver does not receive sufficient signals to maintain an accurate position, the database is called upon for navigation by environmental feature matching. In this scenario, the sensors continue to take measurements and, by combining the knowledge of the last known location, dead reckoning and the sensor’s outputs, the positioning algorithm draws upon the database to estimate a positioning solution. This method is shown in Figure 1 and Figure 2.

Figure 1. Basic workflow mode for collecting data. Source: GPS World

Figure 2. Basic workflow mode for navigation using collected data.

This navigation system relies upon the roads being travelled on a regular basis so that the “maps” created from the sensor’s outputs are kept up to date and therefore valid. The most likely users of this technology would be fleets of vehicles that can share the mapping information. To focus on a typical system, use in emergency vehicles was considered. Knowing your position is vital in an emergency vehicle, and a system that incorporates a back-up to GNSS would be advantageous. The motivation for maintaining a continuous positioning solution is that, when moving within a complex environment, it is necessary to maintain the integrity of the current position. In emergency situations, delays are not acceptable and integrity is vital. There will be no point in time when the vehicle can be delayed to obtain a position fix.

Although this system will be designed with emergency service vehicles such as ambulances and police cars in mind, it could also be used in wider applications such as fleet management and tracking devices. Ultimately, crowd sourcing or cooperative techniques could be used to pool information from different vehicles equipped with the system. With a very large number of vehicles maintaining the feature database, the system could adapt to changes in the environment very quickly.

To reliably achieve meters-level positioning across a range of different challenging environments, a paradigm shift is needed. We need to use as much information as we can cost-effectively obtain from many different sources in order to determine the best possible navigation solution in terms of both accuracy and reliability.

This new approach to navigation and real-time positioning in challenging environments requires many new lines of research to be pursued.

ROAD EXPERIMENT

A set of sensors with a GNSS receiver were attached to a car and driven in closed loops around Stoke-on-Trent on multiple road types over multiple days. The loops were repeated three times on each day on four road types and then repeated over three consecutive days. The sensors used can be seen in Table 1.

Table 1. Sensors used in the road experiments.

The accelerometer, air quality sensor, barometer, dust sensor, light sensors and microphone interfaced with an Arduino microprocessor which outputted the signals from the sensors to a laptop. The Arduino sensors had a data rate of 20 measurements per second. There was an individual accelerometer (attached to the axel of the vehicle) for use in identifying road texture. There are also accelerometers that form part of the inertial measurement unit (IMU), and these were used for dead reckoning.

The onset of movement as recorded from the IMU was used to assist in identifying the beginning of each circuit. During the car journeys, there were two experimenters, one to drive the car and another to monitor the sensors. There were 5–10 minutes between each round; during this time, the sensors would be turned off and then restarted. The equipment was designed for the outputs of the sensors to be post-processed.

The four classes of road were suburban, urban, rural and high-speed road. The route taken and a view from Google Street View showing the general type of landscape traveled through is shown in Figure 3.

Figure 3. The urban road type used in the road experiment.

Figure 3. The suburban road type used in the road experiment.

Figure 3. The rural road type used in the road experiment.

Figure 3. The high-speed road type used in the road experiment.

A road experiment travelled the routes, using GPS receivers with the Arduino, video camera and the IMU so that GPS time could be used as a constant for the various sensors.

WHOLE ROUTE ANALYSIS

The outputs from the sensors were evaluated initially for their cross correlation over the whole route. This process assessed whether the data from different runs over the same terrain were similar and thus had a high cross correlation. This is vital for this map-building method of navigation. This section deals only with sensors that produce continuous output. The next section discusses discrete features.

Cross-Correlation Coefficients. The correlation coefficient (see online version of this article for derivation equations) is used to calculate the cross-correlation of two rounds of sensor data. The cross correalation coefficient is a normalized value. If a signal is correlated with itself, at zero offset (autocorrelation) this would give a value of 1; entirely uncorrelated data gives 0. Signals 180° out of phase would give a correlation value of –1.

The cross-correlation coefficients are shown in Table 2 for all of the sensors. It shows the coefficients for the four different road types using combinations of rounds (round 1 and 2, round 2 and 3 and round 1 and 3 for each three days) from the same days and the average of the coefficients for all the combinations. The sensors with higher coefficients are discussed in more detail in the following subsections. Road signs do not have cross-correlation coefficients; they are treated differently as this is a discrete measurement.

Accelerometer. The magnitude of the acceleration from a accelerometer triad was used in this experiment as a method of measuring road texture. A zoomed-in section of the acceleration as recorded from the accelerometer against the distance traveled can be seen in Figure 4.

Figure 4. Profile from accelerometer attached to axle.

It is difficult to see similarities in the output from the different rounds, although the accelerometer can show movement from stationary to driving and this was used to initialize the sensor outputs from the XSens IMU. This is shown in Figure 5 at 44s.

Figure 5. Accelerometer data showing vehicle setting off.

Barometer. The barometer measures height change of the vehicle. This sensor consistently produced the highest cross-correlation coefficient, shown in Figure 6.

Figure 6. Comparison of height profile over 3 days with minimums set to zero.

Magnetometer. The magnetometer produced data with distinct spikes caused by various magnetic anomalies in the environment being travelled through. This can be seen in Figure 7 for the high-speed road.

Figure 7. Vertical axis magnetic field profile for a high-speed road.

Figure 8 is a zoomed-in section of the magnetometer data from the high-speed road in Figure 7. It shows correlation with an offset of approximately 44m between round 1 and round 3. This is mostly due to synchronization errors between the magnetometer counter and the GNSS receiver clock. This is the reason a second run of the road experiment was completed.

Figure 8. Zoomed-in section of the vertical axis magnetic field experienced on a high-speed road.

Microphone. The microphone was able to pick up clear signals when the vehicle was stationary, and the signal seems to be dependent on the speed of the vehicle. Figure 9 shows the profile from the microphone.

Figure 9. Profile from microphone attached to axle.

It may be possible to combine this data with the accelerometer or odometer data to develop a clearer picture of what sound is resulting directly from the road surface and what is speed related, although this still may not result in a useful feature for this study.

Thermometer. Temperature can vary particularly in a rural environment, seen in Figure 10. Similarities are not consistent across environments as seen from cross-correlation values in Table 2 and are likely to change with the seasons and due to weather conditions.

Figure 10. Temperature profile for rural roads.

Light Sensor. Four light sensors were used in the experiment: upwards, forwards, left and right facing. Figure 11 shows the data from the upward-facing sensor on the high-speed road. There are distinct events where the light level drops. Many of these instances correspond to gantries (bridge -like structures spanning highways displaying speed limits and other information). These features could be treated as discrete, whereby the sharp dips in light level would be treated as momentary events. Some of the information would be lost in treating the ambient light as discrete, but it would make the feature more robust against changing light levels due to shadowing or cloud cover.

If light is treated as a continuous feature, it can be seen in Table 2 that the cross correlation was inconsistent. This is partly due to the effects of changing light conditions. On the days with direct sunlight, the light sensor would reach its maximum intensity and be saturated. This can be seen in Figure 11, and this affects the cross-correlation coefficient calculated.

Table 2. Cross-correlation coefficients for sensor outputs for the four road types.

Figure 11. The upward-facing light sensor profile for the high-speed road in second experiment day two.

Feature outcome. Thermometer data has been discounted as although it gave a cross-correlation coefficient of about 0.5 for the rural route, the other routes had lower cross-correlation values. Similarly, the microphone data had moderate success in high speed and rural environments but not in the other two routes. Therefore it will not be taken forward to the next phase although it could be used in the future if further processing was carried out on the data. As with the microphone, the light sensor had cross-correlation values greater than 0.5 in the rural and urban environments but had lower values in the other two. The success of this sensor is more reliant on the weather conditions than the environment type. At the current point this will not be brought forward to the next stage.

The accelerometer (used to measure road texture) showed no correlation with cross-correlation coefficients of approximately zero (between 0.0 and 0.3). It was useful for use in dead reckoning, but does not illustrate road texture.

The magnetometer and barometer showed the greatest potential for positioning with the highest cross-correlation values consistent over all the environments. These sensors are taken forward into phase 3.

DISCRETE FEATURES

A discrete feature is one where there are environmental events that occur at one position but can repeat multiple times along a route. The discrete feature can either be Boolean (an event occurs or does not) or it can be descriptive (different possible events or the strength of the event). Examples of discrete features include lamp posts, speed humps and shop signage.

In this paper, the discrete feature that will be discussed will be street signs, although the techniques used are applicable to many discrete features. How the signs are identified will also not be discussed in detail in this paper; instead, focus will be on how a sequence of discrete features is used show consistency across a route.

SCANNING METHOD

This section will look at scanning one round to find the region that best matches a region from a different round. Figure 12 illustrates this technique.

Figure 12. Diagram showing the principle used to scan for best match to a pre-set region.

The test data is scanned through the reference data. Cross-correlation coefficients are calculated as the test data is scanned through. The aim is to locate the position of the test data using the reference data for which the position is already known. The output of this exercise gives a cross-correlation profile (cross correlation as a function of position).

This profile can be treated similarly to a probability density distribution of position (although they are not the same) and so gives an idea of the probability of the position at each point in the test data.

Results. Two rounds from the suburban route are shown in this section as an example of the results achieved with the scanning method. Figure 13 and Figure 14 show the cross-correlation profiles for magnetic field and height for day 3 rounds 1 and 2 on the suburban route respectively. The test data region chosen is centered at 1.6 km into the route. The test data region size was 125 m for 4.5 km reference data.

It can be seen that the magnetic field has a number of peaks along the route. The peak with the highest cross-correlation coefficient is at the 1.6-km point (which is the correct position). For the height figure, there are many broad peaks at similar cross-correlation values approximately 700 m apart. The height peaks are broader than the magnetic peaks because the terrain height changes more slowly than the magnetic field.

Ambiguities, Dead Reckoning. The two graphs in Figure 13 and Figure 14 show that there are ambiguities present in both of the features. The majority of the features will have some ambiguities along a route, and so it is important to develop a technique that could mitigate them. One way ambiguities could be mitigated is by using the information available from dead reckoning. The dead-reckoning solution will have a specific position error (which grows with time), and the ambiguities from the features can be reduced by only considering the candidate position within the dead reckoning position uncertainty bounds.

Figure 13. Cross correlation scan for magnetic field on the suburban route with the center of the actual position of the test data shown as blue dotted line.

Figure 14. Cross correlation scan for the barometric height on the suburban route with the center of the actual position shown as blue dotted line.

COMBINING FEATURES

The quality of position information that can be extracted from a particular feature type varies with location. Thus, a better position solution can be obtained if higher weighting is attributed to higher quality features. Factors that will need to be considered include the precision of position that can be extracted from a feature, the level of ambiguity (Are there multiple candidate positions?) and the reliability (how much measurements vary unpredictably with time).

There are multiple ways to combine the scores from different features. Initially, there is the decision as to when in the position estimation process to combine the features. There are two ways to do this: Either combine the scores for each feature, or combine the position estimates for each sensor. The following subsections will describe a number of ways of combining the scores before estimating the position. It will be noted if these techniques could also be used to combine position estimates.

Equal Weighting. A simple combination technique is for each feature score to have equal weighting. The equal weighting used earlier took the two scores and found the average. This way, no single feature will dominate the navigation solution. As the feature scores are not probabilities, the values are not self-weighting, therefore it cannot be presumed that that equal weighting would always provide an optimal position estimation.

Test Data Weighting. This method takes a set of experimental data and empirically determines the weighting coefficients based on the best position solution in this test dataset. The test data would be used to maximize the score of the combined features using weighting at the correct position. This would have the benefit of using real data to determine the weighting, but its strength is based on how representative the test dataset is to the environments that the car will travel in.

Environment Weighting. This would detect the environmental context and use this to select an appropriate set of weights. For example, the presence of many Wi-Fi sources would suggest a suburban or urban environment, while a vehicle speed of 31m/s (70 mph/113 km/h) would suggest that the vehicle is likely to be on a highway. Based on this knowledge, it would possible to use a specific weighting coefficient set is developed for that environmental context.

Cross-Correlation Weighting. This weights each feature according to the characteristics of the cross-correlation coefficients profile obtained using the scanning method described earlier. This enables the weighing to adapt to the quality of the data. Figure 15 shows traits of a set of peaks that affect the confidence in the highest peak being the correct position.

Figure 15. Weighting nomenclature of actual position shown as blue dotted line.

Taking the uncertainty in the current position, only peaks that, for example, fall within 3 standard deviations would be evaluated. The characteristics of the tallest peaks compared with the others would be used to determine a measure of confidence for that feature.

There will be more confidence in the tallest peak (h0) if there is a greater difference between its height and that of the other peaks within the uncertainty range (h1, 2, 3). In Table 3 this is height.

Table 3. Cross-correlation profile weighting showing average distance from true position of the vehicle and the percentage of times the weighted scanning technique calculated the position within 100 m.

The next factor is the number of peaks within the uncertainty range (No. Peaks). The more peaks, the less confidence that the correct peak has been chosen as the position estimate.

The average cross-correlation coefficient within the uncertainty region (γ) would affect the confidence in the estimated position. If the average coefficient value (Av. CC) was similar to that of the highest peak, this suggests insufficient variation in the data being analyzed from that feature.

Finally, the standard deviation could be used. Calculating how many standard deviation (Std Dev) the highest peak was from the mean could provide a weighting value.

Each of these characteristics was looked at separately and compared against the benchmark of equal weighting using the scanning method comparing multiple pairs of rounds on different routes. It can be seen in Table 3 that the standard deviation from the mean provided the best weighting outcomes. To optimize the weighting algorithm, it may be that using a combination of these profile characteristics would provide the best position estimation.

Figure 16 and Figure 17 show examples of cross-correlation profiles; they show high and low confidence respectively. Figure 16 is the cross correlation of data from day three, rounds two and three, on suburban roads. It has a few spaced out peaks over the full profile, and one of the peaks is clearly higher than the others. Figure 17 is the cross correlation of data from day two, round three, and day three, round three, from the high-speed road. It has many similar height peaks all around the value of 0.5.

Figure 16. Good cross correlation profile; few spaced out peaks with one higher than all other peaks.

Figure 17. Poor cross correlation profile; many low similar height peaks.

CONCLUSION

Environmental features have sufficient variability spatially and stability temporally for a database of features to be developed to create a map of the environment. This supports the hypothesis that it is feasible to map a space and then create a feature-mapping and navigation algorithm using a combination of environmental feature sensors, a GNSS receiver and sensors for dead reckoning.

FUTURE WORK

The next step of the project is to develop a feature-matching, mapping and navigation algorithm that incorporates inputs from the multiple sensors, a GNSS receiver, map-matching and sensors for dead reckoning. The algorithm will run collecting sensor data while GNSS receiver data is available, and store this in a database along with location stamps until called upon in times of GNSS receiver signal disturbance. The data from the road experiments will be used for a test database in developing the navigation system.

ACKNOWLEDGMENTS

Debbie Walter is funded by Engin-eering and Physical Sciences Research Council (EPSRC) and Terrafix ltd.

The authors thank Paul Neesham for a method of manually recording street signs seen in video footage and Juliusz Romaniuk of Terrafix for advice and creating hardware that contained the sensors’ carrier frequencies.

DEBBIE WALTER is a Ph.D. student at University College London in the Engineering Faculty’s Space Geodesy and Navigation Laboratory, and a software engineer at u-blox.

PAUL GROVES is a lecturer at UCL, where he leads a program of research into robust positioning and navigation. He holds a Ph.D. in physics from the University of Oxford.

BOB MASON is chief scientific officer and director of Terrafix Limited, holding a Ph.D. in communications and neuroscience from Keele University.

JOE HARRISON is principal radio frequency design engineer at Terrafix Ltd.

JOE WOODWARD is a software design engineer at Terrafix Ltd.

PAUL WRIGHT is a development engineer with Terrafix Ltd., with doctoral degrees in physics and electronics.

May 23, 2017
Raytheon launches WAAS payload to improve GPS accuracy for air travel

Raytheon Company has launched its GEO 6 satellite payload into orbit for its 12-year mission. It is the latest payload to support the Federal Aviation Administration’s (FAA) Wide Area Augmentation System (WAAS), which enhances the reliability and accuracy of GPS signals for directing air travel.

The Raytheon-developed payload is a key element of WAAS, which offers commercial, business and general aviation pilots more direct flight paths, greater runway capability and precision approaches to airports and remote landing sites without dependence on local ground-based landing systems.

“This latest payload launch is the next step in our journey with the FAA to bolster navigation safety and efficiency for commercial and general aviation,” said Bob Delorge, vice president of transportation and support services for Raytheon Intelligence, Information and Services.

In June 2016, Raytheon launched WAAS GEO 5, which was recently accepted by the FAA for integration into the operational WAAS system. Both WAAS GEO 5 and GEO 6 were launched to replace aging satellites and enhance GPS precision for the FAA. WAAS increases GPS accuracy from 10 meters to approximately two meters and supports nearly all of the national airspace.

The WAAS GEO 6 payload is hosted on a geostationary satellite, SES-15, owned and operated by SES. The satellite was successfully launched May 17 from Arianespace’s Guiana Space Center in French Guiana aboard a Soyuz launch vehicle.

May 22, 2017
GSA launches 2017 GNSS Market Report
With an in-depth look at market opportunities and trends across eight market segments, the European GNSS Agency’s (GSA’s) annual GNSS Market Report serves as a key resource for navigating the fast-evolving world of satellite navigation technology and GNSS applications.

The fifth edition, the 2017 GNSS Market Report, was released May 10 by Carlo des Dorides, executive director for the GSA, at the European Navigation Conference held in Lausanne Switzerland.

According to the new report, the growing demand for precise location information, in combination with the ongoing evolution of GNSS technology, means that today’s GNSS market is bigger than ever.

According to the 5th edition of the GSA’s popular GNSS Market Report:
- The global GNSS market is expected to grow from 5.8 billion devices in use in 2017 to an estimated 8 billion by 2020.
- The GNSS downstream market is expected to produce over € 70 billion in revenue annually in 2025. When the revenue created by added-value services is included, this number could more than double.
- The global GNSS downstream market is forecast to grow by more than 6 % annually between 2015 and 2020. Following the declaration of Galileo Initial Services in 2016, chipset and receiver manufacturers and application developers are leveraging Galileo signals, and a number of Galileo-ready devices are already on the market.
- By 2025, the installed base of GNSS devices in drones will reach 70 mln, more than twice the sum of other professional market segments combined.
Regularly referenced by policy-makers and business leaders around the world, the GNSS Market Report serves as the go-to resource for an in-depth look at GNSS market opportunities and trends across an array of essential market segments.

“Providing in-depth information on today’s GNSS market opportunities and a data-driven forecast of its evolution through to 2025, this edition is a must-read for anyone looking to successfully navigate this promising market,” des Dorides said.

The GNSS Market Report takes a comprehensive look at the global GNSS market, providing a thorough analysis per market segment (Location-Based Services, Road Transportation, Aviation, Maritime, Rail, Agriculture, Surveying and Timing & Synchronisation), region and application type, including information on shipments, revenues and installed device base.

The 2017 edition includes such new features as:
- An expanded section on macro-trends like the Internet of Things (IoT), Smart Cities and Big Data.
- Segment-specific user perspectives, with an emphasis on the increasingly stringent demands of today’s GNSS users.
- The unique added-value that European GNSS (EGNOS and Galileo) brings to each segment and how Galileo is already enhancing the functioning of many applications.
- A special feature on the important role that GNSS plays in the growing market of drones (i.e., UAVs/Remotely Piloted Aircraft Systems).
The full 100-page report is available for download free of charge.

Methodology

The GSA GNSS Market Report is compiled by the GSA and the European Commission and was produced using the GSA’s systematic Marketing Monitoring and Forecasting Process.

The underlying market model uses advanced forecasting techniques applied to a wide range of input data, assumptions, and scenarios to forecast the size of the GNSS market in terms of shipments, revenue, and installed base of receivers.

Historical values are anchored to actual data in order to ensure a high level of accuracy. Assumptions are confronted with expert opinions in each market segment and application and model results are cross-checked against the most recent market research reports from independent sources before being validated through an iterative consultation process involving pertinent sector experts and stakeholders.
May 15, 2017
u-blox offers automotive dead reckoning firmware

Positioning chip company u-blox is making available its latest automotive dead reckoning (ADR) firmware for navigation, Telematics, eCall and V2X applications for both OEM and after-market applications.

Firmware ADR4.10 offers real-time, low latency positioning at up to 30 Hz using a combination of multi-GNSS, inertial sensor and vehicle speed data. The release also offers simplified installation, improved accuracy in dense urban environments and new messages for eCall.

ADR4.10 is available now to OEMs using u-blox M8030-Kx-DR professional and automotive-grade chips. By the end of May, it will also be available on NEO-M8L ADR modules, including the new automotive-grade NEO-M8L-02A.

u-blox ADR products are backed by specialist support at local and regional centers.

May 15, 2017
NovAtel SPAN technology to provide land vehicle positioning

NovAtel has released its SPAN Land Vehicle technology for fixed-wheel land vehicle applications. The announcement was made at AUVSI’s Xponential 2017.

SPAN Land Vehicle optimizes integrated GNSS + INS performance for land vehicles during periods of extended GNSS outage, in low dynamic operating environments, or in dense urban canyons. SPAN Land Vehicle ensures that accurate position, velocity and attitude is maintained during such difficult operating environments.

NovAtel uses intelligent vehicle dynamics modelling and its patented Antenna Phase Windup technology to achieve the exceptional performance of SPAN Land Vehicle. The intelligent vehicle modeling identifies inertial measurement unit (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 modelling, corrects for IMU errors in attitude (roll, pitch, yaw).

SPAN Land Vehicle performance can be enhanced even further by adding an external sensor such as a Distance Measurement Instrument (DMI), dual antennas or any other external position, velocity or attitude input. It is available on NovAtel’s entire line of SPAN supported IMUs.

May 15, 2017
Congress increases funding for UAS research, airspace integration

More than $20 million for research on unmanned aircraft systems (UAS) was included in an appropriations package that Congress passed and the president signed into law last week to fund the federal government through the end of the fiscal year on Sept. 30. The funding for UAS research is $2.67 million more than last year’s budget request by the Federal Aviation Administration (FAA) to address a host of research challenges associated with integrating UAS into the national airspace system.

The measure’s section on appropriations for transportation agencies also includes $20 million above the 2016 budget request for the FAA’s air traffic control organization. The increase will provide for the hiring and training of new controllers and accelerating UAS airspace integration. The agreement also includes $11.5 million more than was requested for aviation safety activities for UAS integration, including the addition of six full-time positions to support the certification of new technologies and advance the FAA’s organizational delegation authorization (ODA) efforts and strengthen safety oversight.

May 11, 2017
Intelligent transportation systems require ‘the ego vehicle’

Photo: U.S. Department of Transportation

Image: U.S. Department of Transportation

Most activity so far in the PNT community has centered around the questions of “Where am I?” and “Where am I going?” and “How fast am I going?” Positioning, navigation and timing. Seemingly that should about cover it. But no.

Mapping comes into the picture: “What fixed objects are in my environment?” This is actually a corollary of “Where am I?” though let’s not put too fine a point on it.

All this “I” business. To get to driverless cars and other autonomous vehicles, we will have to look beyond the first person singular, what some researchers call “the ego vehicle.” We must know, with a high degree of precision and certainty, “Where are other moving vehicles?” and “Where are they going?” and “How fast are they moving?” Another order of magnitude, if not several. PNT squared, as it were.

In the fast oncoming intelligent transportation systems (ITS), future driving (very much present and evolving now) will rely on accurate, reliable and continuous knowledge of the position of other so-called road participants. That’s not just cars, trucks, motorcycles and buses, but includes pedestrians and bicycles and who knows what else — skateboards?

The first approaches to this requirement use on-board ranging sensors such as camera/vision systems, radar, laser scanners and more. (Some of this “more” is explored in this May’s print cover story, “Look Around.”) This already calls for a significant level of integration with GNSS and inertial systems of the ego.

But it’s still not enough. A cooperative approach must develop, in which the other road participants actively support the continuous estimation of all relative positions. Not only must they have all the sensors the ego possesses, they must continually communicate all that data with the ego, and conversely. This is what’s called “connectivity.”

It’s almost as if vehicles are becoming sentient, expressive beings. A bit like us. Bringing new meaning to the expression “the automobile as an extension of the self.”

May 8, 2017
Autonomous vehicles drive innovation in the GNSS industry

The May issue of GPS World carries these three expert opinions on the question: How are autonomous vehicles and V2V technologies driving innovation within the GNSS industry?

Chaminda Basnyake

Chaminda Basnyake
Principal Engineer, Market Development,
Locata Corporation

We still have technical and cost versus performance challenges to meet the PNT needs of V2V and AV. Positioning and even timing expectations in deep urban areas are still not met reliably. As a result, ad hoc methods such as HD map-based nav — methods that work but are not scalable — have emerged. Innovations to deal with multipath, signal visibility and geometry are critical. Solutions that enable real-time mapping will be essential for scalable AV deployment.

Curtis Hay

Curtis Hay
Technical Fellow, GPS & Maps,
General Motors

Four key areas the commercial GNSS industry is pursuing include: low-cost, high-volume dual-frequency chipsets; broadly available PPP and network RTK corrections delivered either through mobile IP or satellite; precise maps for highways, urban centers and trunk roads that achieve 10-cm localization relative to WGS-84; and improved integrity monitoring and fault detection. The National Highway Transportation and Safety Administration also released a proposed rule-making with tight standards for GNSS performance: 1.5 meters, 1-sigma confidence.

Jonathan Auld

Jonathan Auld
Director, Safety Critical Systems,
NovAtel

Unlike traditional GNSS applications, automotive positioning requires high-precision accuracy at extremely low cost and size. Most importantly, this performance must be achieved with high reliability while operating in the toughest environments. Solving this positioning challenge is driving innovation in the system engineering of multi-frequency receivers and antennas along with extending performance through sensor fusion with lower cost devices. Additionally, there is significant work in the area of safety and integrity for land-based applications.

Here’s a preview of the V2V countdown article from the May issue, introduced by Chaminda Basnyake, an engineer at Locata Corporation:

The U.S. Department of Transportation (USDOT) released a Notice of Proposed Rulemaking (NPRM) in December 2016 for the deployment of Dedicated Short Range Communications (DSRC)-based vehicle-to-vehicle (V2V) safety applications as part of the connected vehicles (CV) and automated vehicles (AV) initiative. If all goes well, this mean a V2V deployment mandate for new passenger vehicles likely starting in 2021 and reaching all new vehicles within 2–3 years.

Standards required for V2V deployment were published in 2016 or before, including the V2V Minimum Performance Requirements SAE 2945/1, leading the way for commercial product development. The USDOT, which has been the catalyst behind V2V industry R&D starting from the automaker collaboration CAMP (Crash Avoidance Metrix Partnership) in 2001, is conducting CV Pilot programs in New York, Wyoming and Florida. These offer the opportunity for state DOTs, vendors and all other stakeholders to test the technology in real-life scenarios.
Automotive OEMs have been developing this technology for more than a decade, and the NPRM is the beginning of a race toward integrating V2V to production vehicles. Deploying V2V technology requires the close cooperation of OEMs, their suppliers and many other stakeholders.

This article captures the views of major players in the CV marketplace on expected deployment timelines, remaining challenges such as reliable positioning technology, integration with existing systems, and the implications on AV technology.

May 2, 2017
Tallysman introduces NMO mounts for dual- and triple-band GNSS antennas

Tallysman, a manufacturer of high-performance GNSS antennas and related products, released its NMO (New Motorola) mounts for its dual- and triple-band GNSS antennas. NMO mounts are used in a variety of applications such as automobiles, railway cars and emergency vehicles.

With the introduction of this mount, customers can now upgrade existing GPS L1-only antennas to dual (L1/L2) and triple (L1/L2/L5) band GNSS antennas.

The NMO mount is available for Tallysman’s TW3872 (GPS L1/L2, GLONASS G1/G2, BeiDou B1, and Galileo E1) and the TW3972 (GPS L1/L2/L5, GLONASS G1/G2/G3, BeiDou B1/B2, Galileo E1/E5a+b + L-band correction) antennas.
The NMO mount is able to accept a ground plane (also available from Tallysman) to increase the gain of the antenna.
Tallysman antennas are housed in an IP67 compliant housing and are REACH and RoHS compliant.

May 2, 2017
V2V countdown: Major players on how we get there
We asked major players in the connected vehicles marketplace for their views on expected deployment timelines, remaining challenges such as reliable positioning technology, integration with existing systems, and the implications on autonomous vehicle technology.

Curated and introduced by Chaminda Basnayake,
Principal Engineer, Market Development,
Locata Corporation

State of the Industry: Connected Vehicles

Intersection Movement Assist warns the driver if it is not safe to enter an intersection, for example, if another vehicle is running a red light or making a sudden turn. (Image: U.S. Department of Transportation)

The U.S. Department of Transportation (USDOT) released a Notice of Proposed Rulemaking (NPRM) in December 2016 for the deployment of Dedicated Short Range Communications (DSRC)-based vehicle-to-vehicle (V2V) safety applications as part of the connected vehicles (CV) and automated vehicles (AV) initiative. If all goes well, this mean a V2V deployment mandate for new passenger vehicles likely starting in 2021 and reaching all new vehicles within 2–3 years.

Standards required for V2V deployment were published in 2016 or before, including the V2V Minimum Performance Requirements SAE 2945/1, leading the way for commercial product development. The USDOT, which has been the catalyst behind V2V industry R&D starting from the automaker collaboration CAMP (Crash Avoidance Metrix Partnership) in 2001, is conducting CV Pilot programs in New York, Wyoming and Florida. These offer the opportunity for state DOTs, vendors and all other stakeholders to test the technology in real-life scenarios.

Automotive OEMs have been developing this technology for more than a decade, and the NPRM is the beginning of a race toward integrating V2V to production vehicles. Deploying V2V technology requires the close cooperation of OEMs, their suppliers and many other stakeholders.

The following transportation article captures the views of major players in the CV marketplace on expected deployment timelines, remaining challenges such as reliable positioning technology, integration with existing systems, and the implications on AV technology.

Cadillac Communicates: V2V Now, Sensor Sharing Soon

By Curtis Hay
Technical Fellow, GNSS and Precise Maps,
General Motors

General Motors is the first automaker to offer V2V technology in North America with the 2017 interim model year Cadillac CTS. These V2V-equipped vehicles share information to alert drivers of upcoming potential hazards. Cadillac’s V2V uses DSRC and GPS, and can handle 1,000 messages per second from vehicles up to nearly 1,000 feet away. For example, when a car approaches an intersection, the technology scans the vicinity for other vehicles and tracks their positions, directions and speeds, warning the driver of potential hazards.

GM continues to make technology investments in V2V to achieve greater global market volumes. We have been developing V2V technology for the past several years and are exploring potential enhancements to the V2V features currently offered. Nearly all global OEMs are developing V2V today, but market readiness, adoption and technology maturity vary greatly between regions and manufacturers. I expect other OEMs will begin to deploy V2V systems beyond model year 2017.

We believe that autonomous vehicles will require some level of connectivity — there is no way around this. V2I connectivity is required for precise map updates, emergency call alerts, GNSS corrections, remote diagnostics, traffic and weather updates, and many more applications — both existing and emerging. V2V communication will also be an important technology to improve safety and reliability as autonomous vehicles become more broadly deployed.

As a technical challenge, the limitations of GNSS are certainly understood by automakers for applications such as vehicle navigation, stolen vehicle tracking and emergency response services. Many recent advances in vehicle positioning technology mitigate the effects of urban multipath and poor sky view. These include higher quality micro-electro-mechanical systems (MEMS) sensors, low-cost lidar, visual inertial odometry, wheel encoders, precise maps and more GNSS satellites in view.

We believe that high-confidence lane classification is becoming possible even in dense urban environments, thanks to these and other advancements. Infrastructure augmentation will certainly help, and these investments are gradually being made by state and local governments. However, technology development occurs at a faster pace inside the vehicle versus along our roadways.

There is growing demand for low-cost, high-quality automotive cameras and radar components that will be critically important for CV and AVs. I expect some degree of sensor data sharing over V2V will enter the industry within a 4–5-year time frame. Today, not all automotive cameras are designed to provide real-time video output across a high bandwidth interface such as low-voltage differential signaling (LVDS).

Furthermore, DSRC protocol and LTE Release 14 are not yet broadly accepted among competing OEMs. V2V innovations will occur as OEMs see what is possible, and customer demand for safety and reliability increases. Once the auto industry has passed the 50% milestone for market penetration of V2V vehicles, the rate of adoption will be much higher for new vehicle builds.

Denso’s autonomous vehicle research and development ranges from head-up display to voice recognition and human machine interfaces.

Connectivity Paves Way to Autonomy

By Roger Berg
Vice President, North America Research & Development,
Denso International America

As we know, GM offers V2V in the current model year CTS, and Toyota deployed ITSConnect in Japan in 2016. So, multiple OEMS have cars on the road and appear to see the value of V2V.

A retrofit V2V, a universally acceptable U.S. National Highway Traffic Safety Administration (NHTSA)-compliant solution that could be installed at a dealership, is an interesting concept that has been around in recent years. This will allow OEMs to comply with the rule much quicker. However, that concept is easier said than done, and it hasn’t been the focus of the industry up until now.

I see connectivity as nearly a requirement to get to highly AV in the future. On a limited-access highway, connectivity is probably not a requirement, as there are predictable and infrequent “high anxiety” encounters. In an urban setting, however, many other elements complicate the necessary behavior and reaction; and therefore I see the most value from connectivity.

Sensors such as cameras can detect the state of a traffic light with some level of certainty, but often the situation is complicated, such as the need to differentiate between a straight versus a turn signal. Even in highway scenarios, we can see how connectivity can favorably impact use cases like truck platooning and cooperative automated cruise control.

For positioning, it may be that a terrestrial solution will be necessary in difficult GNSS environments such as New York. It’s clear traditional GNSS is not capable of performing at the level required for the cooperative crash avoidance capability that NHTSA desires. Ranging systems that operate as a part of V2I and high-definition maps with lidar could be potential augmentations. I can relate the latter to how humans drive: Although we are not aware of our position, we can certainly drive in Manhattan (with difficulty!) by observing lanes, curbs and other relative

I envision V2V as part of a typical in-vehicle sensor suite at some point without exception; vehicles will eventually communicate what they see with their sensors to others via DSRC. Denso holds a patent that proposes to use on-board sensing to detect the presence of unequipped vehicles and send a proxy basic safety message (BSM) to other vehicles through DSRC.

In the V2V NPRM, NHTSA defines benefits in terms of lives saved under full penetration, but we believe benefits can be shown under much lower levels. For example, in the Ann Arbor Safety Pilot, even with under 5% penetration, anecdotally the University of Michigan buses averaged about one warning every 150 miles during the trial, a significant number of warnings.

ADDITIONAL RESOURCES
Cellular Networks Will Enhance Capabilities

By Roger C. Lanctot
Director, Automotive Connected Mobility,
Strategy Analytics

We think the best-case U.S. V2V deployment scenario might be 2021 — but given the challenges in security management, the ongoing testing of spectrum sharing by the Federal Communications Commission (FCC), and the lack of infrastructure support — we think an even later commencement is likely. This means that early 5G deployments will already be beginning.

It is worth noting that the NPRM provides for alternative technologies as long as the performance requirements are met. The interest in DSRC in Europe has waned significantly, and Toyota appears to be the only company aggressively investing in Japan. China appears to be heading towards 5G for V2X.

In our view, given the vast uncertainties, it makes little sense to proactively add a box that will add cost along with driver distraction and security vulnerabilities. Vehicles will benefit from connectivity regardless of the technology used, but many more miles must be driven before a level of sufficient confidence is reached to integrate V2V with safety systems.

We believe DSRC-based V2V is decades away from delivering a reliable and warrantee-able or life-saving value proposition. Even NHTSA has suggested it may take as long as 20 years before significant value is returned to the manufacturers, let alone the consumer, making the investments today.

We do not think the industry is prepared to integrate safety systems with V2V for a broad range of reasons — GNSS vulnerabilities in urban canyons being one of them. This is the scenario in which additional sensors and high-definition maps can add to location accuracy. Details not only on the road, but also on the location and geometry of buildings, trees, street furniture and more can be gathered by sensors during the mapping process. The vehicle camera and/or lidar sensors can then be used to position the vehicle against this map.

We think a base map will be generated by the mapping entity using vehicles equipped with high-quality sensors and location technology, and then this will be updated by user-gathered data, as well as continued use of the mapping vehicles. This is the approach taken by the likes of TomTom, Mobileye and Civil Maps.

Cellular networks are de facto infrastructure assistants today, and we expect those capabilities to be enhanced. Connectivity is a nice-to-have for AV — not necessary. With the onset of 5G this will change a little bit, but AVs will always have to be able to operate without a connection, in our opinion.

Connected Car a Critical Stepstone to Automated Vehicle and Driverless Driving

By Jonathan Auld
Director, Safety Critical Systems,
NovAtel Inc.

I think some OEMs and Tier1s will integrate the technology in advance of the full mandate and thereby reduce the time to widespread adoption. The benefits of V2V may not be fully realizable at first, but will increase as more equipped vehicles and infrastructure becomes available.

It’s a false assumption that any one technology will resolve CV or AV positioning challenge. The challenging environments and user expectations for high availability and safety will require multiple sensors and systems.

In this context, we see the CV as a critical stepping stone to the AV. CV provides a critical link for V2V communications in low/no-visibility/hidden-object situations as well as a pipe for critical mapping and road network information to the car. As part of this, the GNSS receiver plays a role in being an all-weather absolute position and time reference that can tie all the other sensors together. GNSS has its limitations, as do other sensors, which leads to the multi-sensor fusion approach for accuracy, availability and safety.

The automotive industry’s understanding of GNSS performance is largely driving from the perspective of L1-only single- and dual-constellation receivers. In both the CV and AV use cases, there is a push for more accuracy from GNSS. When moving to a higher performance expectation from GNSS, issues come up that are new to the automotive industry.

For consistent sub-meter-level performance, we start to consider multi-frequency receivers with correction/integrity services supporting them. This is where we see PPP (precise point positioning) as a key technology. Taking advantage of our global PPP correction network for corrections, authentication and safety services will make this performance possible. Also, antenna quality and location become more important. In urban environments where GNSS is less available, we expect a multi-sensory solution to aid GNSS through outages, but still keep lane-level performance as long as possible and safe.

Given the significant challenges on the automotive environment, I would expect that new and innovative ways of gathering and sharing additional information between vehicles and the infrastructure will be developed. It’s entirely feasible that future systems will share as much data as is practical, with the cloud to allow for better map generation and data dissemination. All of this will be driven by the need to keep the systems as available as possible while still maintaining safety.

Dual-Band Carrier Phase for Lane Position

By Rod Bryant
Senior Director, Positioning Technology,
u-blox

We expect to see early adopters integrating the technology ahead of the mandate in selected models such as GM with Cadillac-CTS planned for this year. Depending upon the applications to be supported, DSRC fleet penetration of over 70–80% is probably needed for it to become a truly all-round sensor. That’s why the forthcoming legislation in the U.S. is so important for solving the chicken-and-egg problem, as well as the development of aftermarket V2X.

The combination of CV safety applications with features that use in-vehicle sensors would be a natural evolution. Sooner or later every vehicle will be able to see what others see.

For Level 4 AV systems, GNSS is needed to unambiguously identify the road segment. Highway pilot should not be used off the highway; for lane-accurate positioning with integrity on the urban highway and main roads, we are using dual-band carrier phase positioning with wide area State Space Representation (SSR) corrections and automotive-grade INS. This combination of technologies can cope with the level of interruptions to carrier phase lock and the multipath distortion caused by bridges, signs, trees and buildings in such environments.

As we move deeper into the urban canyon, additional measures will be needed. More advanced multipath mitigation, terrestrial ranging and beamforming techniques could contribute to the solution. V2I ranging is a particularly attractive and obvious example. However other ranging sources could also be utilized. Various beamforming approaches are possible with various levels of disadvantage regarding the accommodation of antenna arrays into the car.

Inevitably, there will be periods of unavailability of GNSS-based lane-level accurate position deep in the urban canyon when required protection limits cannot be met within the required level of integrity risk. It is essential that these are managed properly in the reliance on different sensors at different times and, for lower levels of autonomy, in the interactions between machine and driver.

We see automated driving as a related but separate evolution. The crux of the automated-driving problem is how to manage risk in such a complex scenario. Multiple sensors are being used by OEMs to determine the position of the vehicle with respect to roads and for collision avoidance. Those sensors include GNSS/IMU, radar and lidar, which have overlapping capabilities across conditions. This allows the decomposition of the Automotive Safety Integrity Level (ISO26262 ASIL) requirements.

A combination of all of these sensors is required to meet the stringent safety goals. In that context, V2X will clearly play a role, but may not be seen as a prerequisite. The cooperative nature of V2X operation presents challenges for the application of functional safety methodologies like ISO26262. Partly for that reason, we do not expect the application of V2X to autonomous driving before 2025.
May 2, 2017
M3, Averna join to test auto infotainment

Averna AST-1000.

Averna has entered a strategic partnership with M3 Systems to distribute M3’s StellaNGC GNSS Simulator on National Instruments’ VST platforms for the infotainment segment of the automotive market.

M3 Systems’ GNSS simulator, based on National Instruments’ Vector Signal Transceiver (NI VST), will now be available as part of Averna’s AST-1000 platform, extending its capability to navigation and GNSS testing.

Launched in July 2016, the AST-1000 is an RF solution designed for radio, navigation, video and connectivity testing. Also based on the NI VST, the software-defined AST-1000 supports all common infotainment RF signals, including AM/FM, DAB, RDS, HD Radio and Sirius/XMas, as well as GNSS navigation.

The combination provides a comprehensive solution and enables unprecedented applications for the testing of infotainment systems.

M3 Systems’ GNSS simulator is a good fit to extend the capability of the AST-1000 for navigation testing because both instruments are based on the NI VST, the companies said.

Averna is aiming for an all-in-one platform for the complete validation of infotainment systems, including radio, navigation, audio/video and connectivity testing.

The Averna AST-1000 is available to customers worldwide.

April 26, 2017
Launchpad: Reference clock, receivers, drones

OEM

Rakon RHT1490 series TCXO.

High-Frequency TCXOs

Ultra stable for low jitter and phase noise applications

The RHT1490 series of high-frequency and low-jitter ultra-stable TCXOs are available in frequencies from 50 MHz to 204.8 MHz. It delivers telecommunications-grade stability with a low real mean squared (rms) phase jitter of <200 fs (12 kHz–20 MHz). The platform’s frequency output enables lower system jitter, allowing communication system architects to optimize noise budget and performance. It can serve as a reference clock for SyncE and packet clock requirements (ITU-T G.826x and G.827x). It works with both discrete and integrated IEEE 1588 solutions, providing medium-term stability for low loop bandwidth applications. Its ultra-low noise floor performance, combined with system phase locked loop filtering, helps achieve very low system clock rms jitter numbers required by reference clocks of physical layer devices for high -speed interfaces (40 G and 100 G applications).

Rakon, www.rakon.com

Reference clock

Smiths Interconnect’s 50-channel 8835 GPS reference clock.

Compact and configurable

The 50-channel 8835 GPS reference clock serves satellite communications, defense and wireless applications. It has extreme power and interoperability options while maintaining GPS accuracy and reliability. Tracking GPS, the clock exhibits a frequency accuracy of <1 x 10-12 and a 1 PPS accuracy with <50 nanoseconds real mean squared. The proprietary oscillator steering discipline algorithm can enhance the rms accuracy of either the double-oven crystal oscillator or optional enhanced rubidium oscillator for greater depths of accuracy. It operates from –30° C to +60° C with a terminal node controller GPS receiver port.

Smiths Interconnect, www.trak.com

Survey

Windows tablet

Handheld Group’s Algiz 8X ultra-rugged tablet computer.

Rigorously tested for tough environments

The Algiz 8X ultra-rugged tablet computer is built for field workers who require a powerful, portable computer for mobile tasks. It offers communication features such as LTE and dual-band WLAN, along with an 8-inch projective capacitive touchscreen for outdoor use. Enabling glove mode or rain mode allows for operation in changing weather. The chemically strengthened glass survives an impact test in which a 64-gram steel ball is dropped on the screen 10 times from a height of 1.2 meters. The Algiz 8X has optional active capacitive stylus. Built-in features include Windows 10 Enterprise LTSB; u-blox GPS and GLONASS; WLAN a/b/g/n/ac; BT 4.2 LE; a rear-facing 8-MP camera with autofocus and LED flash; and 4G/LTE.

Handheld Group, www.handheldgroup.com

2D excavating system

Topcon X-52 entry-level machine control system.

Cost-effective grade control

The X-52 entry-level machine control system for excavation features the new intuitive MC-X1 controller, compatible with all brands and models of excavators. Its reliable and rugged TS-i3 tilt sensors detect the precise positioning of the boom, stick and bucket at all times. Later this year, the X-52 will be upgradeable to a full 3D system with GNSS. The X-52 not only allows operators to work faster and with better accuracy, but also promotes a safer work site by keeping grade checkers out of the trenches. The system is designed to pair with the GX-55 touchscreen control box to offer sunlight-readable indicate grade reference in any climate.

Topcon Positioning Group, www.topcon.com

GNSS RTK receiver

Tersus GNSS’ Precis-TX204 receiver.

Integrated display and keypad for configuration without controller

The Precis-TX204 receiver is a light-weight, rugged, all-in-one GNSS receiver with a built-in centimeter-accuracy RTK engine, onboard storage and versatile connectivity. The built-in battery can support up to 10 hours of continuous field work. Up to 16-GB SD card support makes field work easier, and the rugged enclosure enables the receiver to work in harsh environment. The receiver is designed for infrastructure applications such as providing differential data or logging observations; centimeter-level position and velocity information; precise tracking for internet of things; precise navigation for UAV and robotics. It supports GPS L1 and L2, and BDS B1 and B2.

Tersus GNSS, www.tersus-gnss.com

Transportation

Aviation Receiver

Esterline’s CMA-6024 aviation GPS/SBAS/GBAS sensor.

High-performance GPS/SBAS/GBAS for all aircraft

The CMA-6024 aviation GPS/SBAS/GBAS sensor, featuring an embedded VHF data broadcast (VDB) receiver, is a complete, self-contained, fully certified, precision approach and navigation solution certified to Design Assurance Level A (DAL-A). Designed as an easy-to-integrate solution for all aircraft, the plug-and-play standalone unit requires no specialized installation or integration support. The new CMA-6024 provides a navigation solution that is fully compliant with automatic dependent surveillance-broadcast (ADS-B) and Required Navigation Performance (RNP). The CMA-6024 includes SBAS Localizer Performance/Localizer Performance with Vertical Guidance (LP/LPV) and GBAS GNSS Landing System (GLS) GAST-C/D precision approach guidance for all aircraft. Built on the success of the CMA-5024, the CMA-6024 is the next step forward, adding a complete GBAS/GLS solution. All CMA-5024 receivers can be upgraded to a CMA-6024.

Esterline CMC Electronics, www.esterline.com

Electronic logging

GPS Insight’s Electronic Logging Device.

Alternative to paper logs streamlines fleet management

The GPS Insight Hours of Service solution has a feature set designed to streamline fleet management and ensure Federal Motor Carrier Safety Administration (FMCSA) compliance. Hours of Service bundles an Android tablet hardwired to a GPS tracking device. The ruggedized Electronic Logging Device (ELD) tablet features an intuitive user interface to ensure ease of use for all drivers. The management portal is web-based, secure and accessible via PC, tablet and smartphone. Features include messaging between drivers and dispatch; audible and visual directions using designated truck-specific routes; and e-logs combined with GPS monitoring, alerting and reporting. The GPS Insight Hours of Service Solution offers a simple alternative to paper logs and provides many benefits beyond compliance.

GPS Insight, www.gpsinsight.com

UAV

Professional drone

DJI’s Matrice 200 drone.

Rugged platform designed for aerial inspection, data collection

The Matrice 200 drone series (M200) is built for professional users to perform aerial inspections and collect data. The folding body is easy to carry and set up, with a weather- and water-resistant body for field operations. It offers DJI’s first upward-facing gimbal mount, for inspecting the undersides of bridges, towers and other structure. It is compatible with DJI’s X4S and X5S cameras, the high-powered Z30 zoom camera and the XT camera for thermal imaging. A forward-facing first-person-view camera allows a pilot and camera operator to monitor separate images on dual controllers. Obstacle avoidance sensors face forward and up and down, and it has an ADS-B receiver for advisory traffic information from nearby manned aircraft.

DJI, www.dji.com

UAV data analysis tool

PCI Geomatics’ STAX UAV image alignment and analysis tool.

Designed to ease image alignment

The STAX UAV image alignment and analysis tool provides automated tools for aligning and analyzing UAV imagery without a full photogrammetric software suite. STAX was built to address the challenges of collecting and aligning multiple UAV surveys of the same location over time. By automating the alignment process, UAV operators can reduce or eliminate the use of ground control points that are traditionally installed and measured in survey sites. Relative corrections can be applied by using one of the surveys in a stack as a reference. Alternately, a highly accurate reference image of similar resolution over the area of interest can be used to automate the image alignment process. Once multi-pass UAV surveys have been aligned, customers can accurately make comparisons between surveys to measure changes over time or perform feature extraction. STAX provides tools to calculate vegetation indices as well as visualization and basic cartographic capability. Stacked data sets
can be exported for deeper analysis.

PCI Geomatics, www.pcigeomatics.com

SATCOM terminal

Gilat’s BlackRay 72Ka.

Enables long-endurance missions for very small UAVs

The miniature, lightweight BlackRay 72Ka terminal enables long-endurance missions for very small UAVs. The ultra-compact airborne SATCOM terminal for unmanned aircraft systems delivers exceptional throughput for its size. Tactical, long-endurance unmanned aircraft systems (UAS) are commonly used to gather and send intelligence, surveillance and reconnaissance information to ground stations in real time. Reliable, high-performance satellite communications are crucial for ensuring uninterrupted broadband connectivity in beyond line-of-sight missions. Weighing less than 5 Kg, the BlackRay 72Ka combines high performance and throughput with minimal footprint.

Gilat Satellite Networks, www.gilat.com

Hydrogen drone

MMC’s HyDrone 1800.

Long endurance aircraft equipped for military applications

The carbon-fiber HyDrone 1800 is designed for use in tough conditions. The drone is wind-resistant, rain-resistant, cold-resistant and lightweight. Its hydrogen fuel-cell technology provides a flight endurance of 4 hours — 50+ hours when combined with MMC tethered technology. The HyDrone 1800 achieves extended flight time while maintaining altitude limits of 4,500 meters with a payload capacity of up to 5 kg. Constructed for safety and durability, an auxiliary lithium battery starts the fuel cell and provides a backup power source. Hydrogen drones can be flown in extreme temperatures from –10° C to 40° C. Payloads include a thermal imaging camera, low light camera, laser equipment or zoom camera, making the system suitable for many military applications such as intelligence gathering, border patrol, aerial fire support, laser designation or battle management services to tactical military operators. MMC also offers packaged solutions in target acquisition and reconnaissance technology (ISTAR).

MMC, www.mmcuav.com

April 25, 2017