For the automotive industry, Teseo VI chips and modules will be core building blocks of advanced driving systems (ADAS), smart in-vehicle systems, and safety-critical applications such as autonomous driving. They have also been designed to improve positioning capabilities in multiple industrial applications including asset trackers, mobile robots for home deliveries, managing machinery and crop monitoring in smart agriculture, timing systems such as base stations, and many more. 

“Our new Teseo VI receivers represent a real breakthrough among positioning engines for several reasons: they are the first to integrate multi-constellation and quad-band signal processing in a single die; they are the first to embed a dual-Arm®-core architecture enabling both very high performance and ASIL-level safety for assisted and autonomous driving applications. Last but not least, they embed ST’s proprietary embedded Non-Volatile-Memory (PCM), thus delivering a very integrated, cost-effective, and reliable platform for new precise-positioning solutions,” said Luca Celant, Digital Audio and Signal Solutions General Manager, STMicroelectronics. “ST’s new satellite-navigation receivers will support exciting, advanced capabilities in automotive ADAS applications and enable many new use cases being implemented by industrial companies.” 

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This strategic alignment strengthens the delivery of advanced reality capture solutions across a broad spectrum of industries. Professionals in surveying and mapping, architecture, engineering, and construction (AEC), energy and utilities infrastructure, and public safety and forensics will all benefit from enhanced access to these integrated technologies.

“The integration of Topcon’s precision positioning technology with Pix4D’s photogrammetry expertise is another great example of the type of collaboration the geospatial industry has always thrived on,” said Murray Lodge, executive vice president of Topcon Positioning Systems. “This will provide professionals with seamless access to industry-leading solutions that combine our complementary technologies.”

“The agreement on close collaboration with Topcon marks an important milestone in Pix4D growth strategy,” said Andrey Kleymenov, CEO at Pix4D. “A combination of precision positioning technology from Topcon and advanced photogrammetry and GeoFusion algorithms from Pix4D creates a powerful set of solutions for professionals in the utilities, infrastructure, and horizontal construction markets globally.” 

The agreement enables customers to access Pix4D’s advanced photogrammetry software solutions through Topcon’s established global distribution network, streamlining the procurement process for end-users while ensuring comprehensive technical support.

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These locations can include brownfield sites, unsafe environments like peat bogs, and new and aging infrastructure elements, where ground settlement and other perturbations can have serious consequences.

Speaking at a recent project presentation organized by the European Space Agency (ESA), GVL’s Managing Director Paul Bhatia said current solutions such as conventional PNT and RTK base stations are expensive and not well-tailored to varied applications. “AIPLAN combines a custom-built COTS [consumer off-the-shelf]-based GNSS receiver with InSAR [interferometric synthetic aperture radar] technology, fine-tuned using AI and ML [machine learning] to deliver better results,” he said. InSAR is a technique for mapping ground deformation using radar images of the Earth’s surface from orbiting satellites.

AIPLAN geodetic receivers and purpose-designed rovers collect GNSS data, while InSAR point time-series data is collected via corner reflectors and other natural reflectors. All data is uploaded to AIPLAN’s unique AI/ML system via the cloud, where pattern recognition and anomaly detection are employed to smooth errors in the data and provide algorithm training.

All points covered

The project set positioning accuracy targets of 1-5mm in plan and 2-5mm in height. Low-cost GNSS modules were used to keep overall hardware costs down. All equipment is built waterproof and rugged, to withstand deployment to challenging environments. The system is designed to provide real-time updates, with daily position reports. The developed software is easy to configure and user-friendly, accessible via mobile phone or laptop.

The project undertook test campaigns in a variety of settings in the UK, calibrating and assessing GNSS base-stations, rovers and dual InSAR corner reflectors at the Spen pig farm at the University of Leeds, at Snake Pass, a section of the A57 in the Peak District, opened in 1821, and at the Very Light Rail National Innovation Centre in Dudley, West Midlands.

All the results, Bhatia said, were positive. AI- and ML-based AIPLAN systems delivered a high accuracy of 3-4mm, in both horizontal and vertical position measurements, and a newly prototyped visualization suite provided valuable time-series data for the monitoring points. The new tools, he said, are suited to task and can also serve in the monitoring of a variety of geohazards such as landslides and earthquakes.

The AIPLAN project was funded by ESA’s NAVISP program, which supports competitiveness in the European PNT-related industries.

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GPS interference isn’t a new threat, but until recently, it’s mainly been academic and military concerns. Now, it’s becoming a civilian concern as well, with aviation impacted more every day.

There’s a new sense of urgency to combat these threats, with groups like the U.S. Department of Transportation (DOT)’s Center for Automated Vehicle Research with Multimodal AssurEd Navigation (CARMEN+) leading the charge to develop and adopt complementary PNT (CPNT) technologies to strengthen and augment GNSS. 

CARMEN+, led by Ohio State University (OSU) and Director Zak Kassas, a professor of electrical and computer engineering at OSU, is focused on addressing the PNT and cybersecurity challenges highly automated transportation systems (HAVs) face. Along with OSU, the consortium is made up of the University of California, Irvine; University of Texas at Austin; and North Carolina A&T. Experts in cybersecurity, PNT, automotive and transportation are studying the risks to HAVs, offering solutions and making recommendations for future standards and guidelines for cyber resilient PNT solutions. 

CARMEN+ University Transportation Center (UTC) held its annual symposium on October 23 and 24—kicking off the event on International GNSS Day—to showcase how CARMEN+ is addressing the critical need for CPNT and cyber resiliency. Attendees gathered at the Blackwell Inn and Pfahl Conference Center on OSU’s campus to hear from various keynote speakers, take in lively panel discussions and view student poster presentations. There was also a cake to celebrate ION’s GNSS day, tours of facilities including the ElectroScience Laboratory, the Center for Automotive Research (CAR), the Transportation Research Center (TRC) SmartCenter and the OSU Aerospace Research Center. Attendees were also treated to dinner and a tour at the OSU Buckeye’s football stadium, “The Shoe.” 

CARMEN+’s Impact 

Kassas kicked off the symposium with an overview of CARMEN+ and the growing threat of spoofing and jamming. He highlighted the first incident of GPS spoofing impacting civilian aircraft, which happened in September 2023, and how the threat has continued to grow since. He also talked about recent work OSU has completed in collaboration with the Air Force, where researchers demonstrated how terrestrial signals can be used for navigation, particularly cellular. An article about that work, “Protecting the Skies: GNSS-Less Aircraft Navigation with Cellular Signals of Opportunity,” was featured in a recent issue of Inside GNSS. 

Kassas also gave an overview of CARMEN+ and its four main thrusts: identifying existing and emerging cybersecurity threats; analyzing threats and cybersecurity risks; developing cyber resilient mitigation methods; and testing and validating solutions in real-world cyber compromised environments. 

During his talk, Kassas announced CARMEN+ was recently awarded funding for a demonstrator. 

“The aircraft will be a living lab,” Kassas said, “to demonstrate CARMEN+ research for how to navigate without GPS.” 

Balasubramaniam Shanker, chair of electrical computer and engineering at OSU, followed Kassas with an overview of the ElectroScience Lab and the work done there, with CAR Director Giorgio Rizzoni also providing a program overview and update. 

The Future of Transportation 

Karen Van Dyke, director, Positioning, Navigation, and Timing (PNT) and Spectrum Management in the DOT’s Office of the Assistant Secretary for Research and Technology (OST-R), provided an update on what the agency is doing to advance CPNT technologies. She talked more about the growing spoofing and jamming threat, setting the stage for why the work CARMEN+ is doing is so important. 

PNT is at the heart of the future of transportation, she said, and imperative to reaching goals like net zero fatalities. Today, there are about 40,000 vehicle deaths a year in the U.S.

DOT is putting together a strategic PNT plan that should be published by the end of the year, Van Dyke told the group, with goals that include building resiliency, addressing PNT cybersecurity and ensuring spectrum availability and protection for PNT. 

DOT embraces the Protect, Toughen and Augment (PTA) principles developed by the National Space-Based PNT Advisory Board, but also recognizes it needs to go a step further. Developing CPNT technologies doesn’t do much good if they’re not put into use, making adoption a critical factor. 

Van Dyke also discussed the U.S. Government’s Executive Order 13905 on Strengthening National Resilience Through Responsible Use of PNT Services, the CPNT Request for Information (RFI) and Request for Quote (RFQ), and the contract awards given to nine vendors to further develop and test CPNT technologies. 

“The Executive Order,” she said, “challenged us to be able to withstand any disruption, denial or manipulation to any GNSS service.” 

Van Dyke stressed the importance of live sky spoofing and jamming tests and said she’s excited to have the CARMEN+ demonstrator airplane available for future testing as well. 

DOT released a CPNT action plan last year based on feedback from a roundtable where stakeholders discussed the barriers to adopting CPNT. The plan addresses those barriers and promotes widespread adoption of CPNT technologies, focusing on five areas: developing safety-critical PNT standards for transportation services; developing a PNT vulnerability and performance testing framework on demonstrated and suitable complementary technologies; conducting vulnerability performance assessments; developing PNT performance monitoring capabilities to ensure PNT services provide operational resilience and achieve safety critical standards; and establishing a Federal PNT Services Clearinghouse.

The RFI and RFQ came after that action plan was released, with vendors awarded contracts from the Volpe Center to test their technology at three different test ranges—federal, critical infrastructure and commercial—as part of the plan’s Rapid Phase. In the next phase, additional vendors will be awarded contracts, Van Dyke said. 

Through a partnership with the Department of Defense, DOT is working to create a GNSS Operational Awareness Tool (GOAT) that will determine where interference is occurring in real time. Users will still be asked to report outages, but with the tool, the government will be able to confirm there’s an interference issue in that area or that something else may be causing the disruption.

“The Dallas [airport spoofing incident] really sparked a fire at the U.S. Department of Transportation,” Van Dyke said. “We’ve made good progress in the last two years, but there’s more work to be done.” 

Harold “Stormy” Martin III gave a U.S. Space-based PNT Policy update, going over the age and capabilities of in-orbit GPS satellites, Space Policy Directive 7 and what that entails, the executive order and the U.S. Government’s National Standards Strategy for Critical and Emerging Technology, which includes PNT. 

Improving Intersection Safety

Chris Atkinson, the DOT’s deputy director for technology, Advanced Research Projects Agency—Infrastructure (ARPA-I), gave an overview of the Intersection Safety Challenge and how combining sensors, including navigation sensors, could help reduce accidents at intersections. Perception, vision, AI and machine learning are among the technologies being explored to protect vulnerable users. Currently, about 27% of roadway fatalities happen at or near intersections, he said.

“We’re deploying low cost sensors, cameras, radars, LiDAR and infrared, to develop a system that can predict the paths of all actors,” Atkinson said, “and then send an alert if there’s a potential conflict.” 

The program is now in stage 1B, data collection. If at least one system emerges as viable (which Atkinson expects will happen) there will be a stage 2 for field testing, with the goal of deploying a solution in five years. The 15 project teams selected to compete tested emerging technologies at the Turner-Fairbank Highway Research Center (TFHRC) as part of stage 1B. The highly instrumented intersection offers a realistic, safe environment for testing various scenarios and collecting a large amount of data. 

Safety is the No. 1 priority, Atkinson said, and these emerging technologies add another layer of protection that can be used alongside other safety initiatives, such as improving lane layouts and sight lines. 

Engaging Panel Discussions 

The symposium continued in the afternoon with two panel discussions: one on PNT and cybersecurity and another focused on automotive and transportation. For both panels, CARMEN+ PIs gave two-minute lightning updates on their areas of focus, and then opened it up to the audience for questions. The format led to lively discussions about safety, promising CPNT technology, AI and other topics. 

After the sessions, the group enjoyed cake in honor of ION’s GNSS Day before taking in student poster presentations and then tours of the ElectroScience Lab and CAR facilities. Attendees had the opportunity to talk with researchers about the facilities and the projects they’re working on, as well as learn more about successful CAR student-led projects and how they’ve fared at competitions over the years (the answer is very well). 

Day one ended with attendees enjoying time taking pictures on the field at The Shoe. The symposium concluded the next day after tours of the TRC Smart Center and the OSU Aerospace Research Center. 

Get Involved 

Groups like CARMEN+ are helping to advance the solutions needed to protect GNSS. The research they do has become even more critical as GPS interference events continue to rise and impact civilian applications. To learn more about the group or how you can get involved, visit utc.engineering.osu.edu.

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MARK LOONEY, APPLICATIONS ENGINEERING MANAGER, INERTIAL MEMS TECHNOLOGY GROUP, ANALOG DEVICES

GPS and Inertial sensing are key parts of Guidance Navigation Control (GNC) systems in emerging autonomous vehicles (AV). For position tracking and other sub-functions in the GNC, establishing a common time reference between GPS position updates and inertial measurement units (IMU) data sampling can be an important objective, especially in situations that value the tightest level of coupling between these two inertial tracking sub-system elements.

While this seems like a simple task, there are two important things to consider when seeking to synchronize these two sensing functions: (1) both systems tend to operate autonomously from one another and (2) some IMU-supported functions require much higher sample rates than those in GPS systems. GPS systems typically update at rates that are between 1 and 20Hz. IMU update rates are typically well beyond 1,000Hz. 

The solution to the first challenge of synchronizing two “typically independent and autonomous functions” is establishing external control of data sampling as a requirement when selecting a MEMS IMU for the platform. Using the GPS clock and a phased locked loop function to scale up to a sample rate that supports all possible use cases for the IMU is the solution to the second problem. 

Recently released IMUs contain functions that can address both challenges by including the clock scaling function inside the clock management function. Recognizing potential value for this type of synchronization can lead to a specific design requirement for connecting the GPS clock reference and the MEMS IMU, even in early prototyping. Establishing this connection will provide software developers with the option of using this feature when operational environments warrant such tight coupling while retaining the value of higher IMU sample rates. 

MEMS Inertial Measurement Units

MEMS IMUs typically include triaxial angular rate (gyroscope) and linear accelerometer sensing functions. Figure 1 provides an example of MEMS IMU axial definitions, including the direction/polarity of each sensor [1]. When used in the GNC system for an AV platform, the Cartesian coordinate assignments from Figure 1 often translate into roll (X), pitch (Y) and yaw (Z) axial titles. MEMS IMU data sampling and processing rates are often driven by unexpected motion artifacts, such as vibration or underdamped mechanical response to bumps and abrupt changes in the terrain. Even in benign environments, such as a warehouse, under sampling the response of running over a small board or a tool left on the ground can lead to steering errors and require human-level intervention to restore full operation.

MEMS technology has always offered breakthrough levels of size, weight, power, cost, and ease of use but now is starting to reach performance levels that overlap with more contemporary technologies, such as Fiber Optic Gyroscopes (FOG). This performance enhancement is causing MEMS IMUs to be a key enabler for scalable AV platforms, such as local “last mile” delivery platforms. 

Autonomous Ground Vehicles

The dull, the dirty, the dangerous. That has been the classic answer to the question, “What do autonomous vehicles do?” This description covers a wide range of missions in airborne, surface marine, submarine, and ground-based operating environments. For simplicity, let’s focus the rest of the discussion on a single platform type: autonomous ground vehicles (AGV) that will operate outdoors through complex terrain on dirt trails/roads. Perhaps ironically, AGV GNC platforms are still using many of the same functions published 10+ years ago, but with substantial advancement in all these functions. An outdoor cargo AGV may have any combination of the following functions: GPS, RTK, LiDAR, radar, wheel-based odometry, video, air speed and IMUs. 

AGV Use Cases for IMU 

Steering Feedback

The process of converting measured wheel speeds and direction into a steering solution is called reverse kinematics [2]. The precision of the reverse kinematics process relies on several factors, which are often difficult to control. Wheel diameter varies with tire pressure and temperature. Mechanical gear backlash can introduce asymmetry between forward and reverse motion on each wheel. In addition to the mechanical limitations of this system, wheel slippage also can introduce substantial errors in the steering feedback loop. This is especially true for platforms that are navigating over loose dirt surfaces and have time pressure that forces “best available” velocity. On an AGV, the MEMS IMU’s yaw (z) axis gyroscope often aligns with the primary steering axis, which allows the GNC system to track angular changes during commanded maneuvers. When operating in this manner, IMU sample rates are often much greater than the bandwidths of the steering feedback loop. 

Terrain Compensation 

GPS antennas must be on the top side of an AGV to have the best available access to GPS satellite-driven signals. When AGVs are navigating through complex terrains, the resultant roll and pitch angles (away from horizon) will cause alignment error between the GPS antenna and the steering references on the ground. Terrain compensation systems use both accelerometers and gyroscopes to track the attitude angles in real time so the GNC can correct the difference between the GPS location and the steering reference point on the vehicle. 

Position/velocity tracking 

Most outdoor platforms have regular access to GPS, but there are a number of use cases in which MEMS IMUs contribute to position and velocity tracking between GPS updates and partial GPS denial mitigation strategies. Strapdown navigation is one of the most common techniques for using MEMS IMUs to estimate velocity and position. The strapdown architecture uses gyroscopes to track and remove the gravity vector from the accelerometers so gravity will not contribute to the integration (velocity) and double integration (position) error growth models. 

MEMS IMU Performance Advancement

Given the increasing dependence that AV platforms are placing on both short-term and long-term performance attributes, it is important to understand key performance attributes when developing selection criteria for the MEMS IMU. Since FOGs inspired the vision for early development of high-performance MEMS IMUs, Appendix C of IEEE-STD-975-1997 provided the basis for early definition of many MEMS IMUs performance attributes. As the vision for MEMS IMUs expanded out of automotive safety systems and into gaming and fitness tracking applications, there was extraordinary integration along with reduction in size, weight, power and cost. Because these applications focused on tracking simple, repetitive motion profiles, the most important performance attributes focused on short-term noise and stability attributes. 

For example, Angle Random Walk (ARW) describes the rate of drift from integrating the random noise of a gyroscope, with respect to time. Rate Noise Density (RND) describes the magnitude of the noise in spectral terms for those trying to assess loop bandwidth for angular jitter contribution from the noise in their feedback loops. In-Run Bias Stability (IRBS) comes from the minima of the Allan Deviation curve and quantifies the best-available observability of bias error under static inertial conditions and benign environments. 

For AV platforms that have simple mission profiles and can afford the latency times associated with correction feedback that comes from other sensor platforms, these noise/stability metrics are the primary points of interest. When the mission profile has demanding inertial attributes, operates in challenging environments and the consequences of failure are elevated, GNC systems are sensitive to a much broader range of condition-dependent behaviors. Turn-on drift, Thermal hysteresis, long-term and vibration rejection are examples of these behaviors [3]. 

Turn-on drift typically comes from thermal settling but can have other contributors. This typically follows a first-order, decaying exponential, so the key attributes to understand are the initial error and the time constant. This error source typically has a deterministic model, but the key attributes are often dependent on both ambient temperature and proximity to thermal equilibrium. Thermal hysteresis is the difference between the sensor’s bias when moving up and down, in temperature. This attribute is dependent on thermal ramp rate and other mechanical stress attributes, which make it exceedingly difficult to model. Long-term aging typically comes from a combination of electrotonic drift and mechanical settling. The dependence on mechanical settling means thermal shock and other drastic changes in the force profile can change the drift model over time. The bottom line is this error window requires bias estimation loops to manage it. Finally, Vibration Rectification Error (VRE) is the most common metric for quantifying vibration rejection in both accelerometers and gyroscopes. This attribute typically impacts drones but can influence AGVs as well. 

Table 1 shows an example of the type of advancements in MEMS IMUs over the past generation of devices [1]. The net result of these improvements is that in the most demanding environments, system algorithms can reduce latency and convergence times in the estimation loops, widening the conditions the platform can operate through. 

MEMS IMU Operation

Modern MEMS IMUs, such as the ADIS16550, contain complete “motion to calibrated bits” signal chains that provide AV GNC systems with simple, configurable access to inertial sensing. They typically only need a decent power supply to self-initialize and begin producing quality data without requiring any external configuration. That data is available through communication channels that most embedded platforms support with minimal configuration. Figure 2 provides an example of a MEMS IMU functional block diagram. 

MEMS sensors typically rely on tiny spring-tethered “proof masses,” which provide a center-tap for a differential capacitive network. When these devices experience acceleration, the proof mass sets back from the acceleration, causing a differential imbalance in the distances from each side of the fixed frame. In their simplest form, a modulation signal feeds through the center tap (proof mass) and through the capacitive path into two ends of the differential capacitor path. After demodulation, the residual signal represents the acceleration. For gyroscopes, the control and modulation schemes are a bit more complex, but for those that rely on the Coriolis acceleration, the net result is the same.

After demodulation, the residual acceleration (or angular rate) signal goes through an Analog Front End (AFE), which applies filtering to help manage noise and establish the desired sample rate to signal bandwidth ratios. The AFE also applies the signal to optimize the resolution of the digital portion of the signal chain for a given measurement range for each sensor.

Once the signals are through the analog to digital conversion process, they typically go through the primary digital filtering stage and then into the calibration and alignment process. On high-performance MEMS IMUs, calibration typically comes from an extensive inertial testing process, which observes bias, sensitivity (scale factor) and alignment at several different temperatures over the operating temperature. This information feeds into computation of sensor-specific, temperature-controlled correction polynomials for correction value, in the following calibration formula:

• ω_XC, ω_XC, and ω_XC represents the calibrated gyroscopes for each axis.

• ω_XR, ω_XR, and ω_XR represents the calibrated gyroscopes for each axis.

• b_XC(T), b_YC(T), and b_ZC(T) represent the bias correction function, which is typically temperature-dependent and can be implemented via look-up table or polynomial function. 

• m₁₁(T), m₁₂, m₁₃, m₂₁, m₂₂(T), m₂₃, m₃₁, m₃₂ and m₃₃(T) represent the sensitivity and alignment correction functions. The diagonal terms are typically temperature-dependent, while the off-diagonal terms are typically constant, with respect to temperature. 

Finally, some IMUs provide a data-ready indicator function, which can help synchronize data collection and sampling in the GNC’s host processor. Figure 3 provides an example of such a signal, which pulses to an inactive state to indicate when the IMU’s processor is loading new data into the output registers. The inactive time provides a “do not access” time and the second edge of the pulse (inactive to active) can trigger the interrupt service routine (ISR) on the AGV GNC’s host processor. 

IMU Data Synchronization

In addition to the basic signal flow, Figure 2 also shows three different options for driving data sampling and primary digital processing functions: Internal (crystal), Direct Sync and Scaled Sync (portrayed as GPS Reference). The internal clock source is the most common approach, as it enables fully 
autonomous operation without requiring any external signals or commands to produce data. In the ADIS16547 IMUs the natural sample rate is 4,000Hz. This clock source comes from dividing the crystal frequency. 

The Direct Sync functions allows an external clock source to drive the data sampling and processing. For optimal operation on the ADIS16547, the optimal external clock rate is 4,000Hz, but it will operate over a wider range of 3,000Hz to 4,500Hz. 

The Scale Sync function allows AGV GNC systems to provide a much slower clock reference (such as GPS or Video sync) while maintaining the optimal sample rate for the gyroscopes and accelerometers. Figure 4 provides an illustration for how this can work when feeding the ADIS16547 with a 10Hz reference signal from the GPS receiver. In this example, the GNC’s host processor will configure the ADIS16547 to operate in Scaled Sync mode and will configure the clock scale factor to 400 to cause the data sampling and processing to be at 4,000 Hz. This example also uses the decimation filter to average 10 samples (4,000Hz) together to produce each fresh data set for an output data rate (ODR) of 400Hz. This solves both problems of synchronization and reduces the output data rate to ease the processing burden on the GNC processor while retaining all the benefits of the higher sample rates in the initial stages of the IMU’s signal chain. 

Conclusion 

Given the performance advancement and functional integration, AV system architects are now able to reap the benefits of synchronizing MEMS IMU data sampling with GPS reference signals while retaining the benefits of sampling MEMS IMUs at higher sample rates. Having the flexibility to turn this connection on and off with simple register write cycles enables platforms to quickly transition to and from environments that deny GPS. In addition to software programmable switching between clock sources, programmable scale factors also enable quick reconfiguration when different GPS receivers are in use. 

References

(1) Analog Devices Datasheet ADIS16550 Autonomous Grade, Six Degrees of Freedom Inertial Sensor, https://www.analog.com/media/en/technical-documentation/data-sheets/adis16550.pdf 

(2) Looney, Mark, “Inertial Sensors Facilitate Autonomous Operation in Mobile Robots,” Analog Dialogue, 2010, vol. 44. 

(3) I. Prikhodko, J. Geen, C. Merritt and S. Zhang, “Vibration Immune, Long-Term Stable and Low Noise Synchronized Mass MEMS Gyroscope,” 2021 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Kailua-Kona, HI, USA, 2021, pp. 1-4

(4) Analog Devices Datasheet ADIS16547 Tactical Grade, Six Degrees of Freedom Inertial Sensors, https://www.analog.com/media/en/technicaldocumentation/data-sheets/adis16545-16547.pdf

Author

Mark Looney joined Analog Devices, Inc. in 1998 and is currently the Application Engineering Manager for the Inertial Sensing Technology Group. He earned Bachelor of Science (1994) and Master of Science (1995) degrees in Electrical Engineering from the University of Nevada. Since joining the industry in 1995, Mark has worked in development, characterization and system level integration of inertial sensing, high-speed analog-to-digital conversion, clock management, power management and embedded processing technologies. Prior to joining Analog Devices, Mark was a design engineer for the Interpoint Corporation and was a founding member of IMATS, a vehicle fleet and traffic solutions start-up company.

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Applications requiring stringent requirements in terms of positioning and navigation are on the increase, especially in the rapidly evolving transport sector, with more and more autonomous vehicles and machine control applications on the horizon. The challenge lies in managing transfer functions and minimizing environmental issues to obtain the kind of clean measurements that are vital for advanced PNT engines such as Kalman and particle filters, DPE, and PPP/RTK.

The project included a comprehensive review of the state-of-the-art, evaluating AI-enabled algorithms at various stages of the receiver chain. Researchers used a newly designed AI GNSS test bed to investigate, trial and benchmark various AI algorithms. The project ultimately selected three algorithms, out of nine originally considered. The selected algorithms were designed to enable accurate carrier phase measurements and mitigate the impact of multipath interference in developing a reliable tracking loop.

In-depth evaluation

The first algorithm used a supervised Multipath (MP) regression technique with a convolutional neural network (CNN) to estimate pseudorange and mitigate multipath interference after correlation. The second algorithm employed post-correlation processing with supervised regression to generate an ideal auto-correlation function (ACF) output, enhancing its understanding of signal features and characteristics through CNN analysis. The third algorithm, utilizing gradient boosted trees, estimated pseudorange error relative to the reference Rx 1 by utilizing a RINEX format and applying supervised pseudorange regression on four receivers. Two variations of the third algorithm (with and without AI) outperformed a prepared u-blox solution by several meters in each east-north-up (ENU) component at a 95% confidence level.

Data was collected in real-world driving situations, in city centers as well as in rural settings in the UK. Complex environments included deep urban canyons and country roads with thick tree cover. Tests included use of E1, E5a and L5 signal bands and of multi-antenna systems.

While the results of the testing were mixed, said GMV’s Oliver Towlson, speaking at the final project presentation, the outcomes do provide valuable knowledge for successful algorithm development and the establishment of a multi-purpose, multi-device testbed for data acquisition and processing. Occasional significant improvements in PVT performance were achieved, particularly in controlled environments, while the overall impact of AI on the algorithms’ performance was not substantial compared to classical GNSS PVT methods. AlnGNSS was carried out under the ESA NAVISP framework, aimed at supporting innovation and competitiveness in the European PNT sector.

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One of the specific objectives of the new scheme is to improve incentives for transitioning the country’s HGV fleet towards lower emission vehicles. It will also help reduce the impact these vehicles have on infrastructure and road wear costs, as well as their noise impact.

The project is being managed by Sund & Bælt, a Danish government-owned company that manages the Storebælt and Øresund road links. The company will be responsible for both implementation and operation of the new system. A GNSS onboard unit (OBU), costing around €150 including installation, will be mandatory for HGV operators, with a cheaper, self-installed OBU option also planned. Manual or pre-paid ticket options will likely not be available.

No standing still

A first step in the procurement process has already been initiated and will continue until early 2024. Sund & Bælt have engaged in talks with a number of suppliers with experience in the delivery and maintenance of GNSS and distance-based tolling solutions. Key technical challenges include GNSS data handling, map and toll context data and map-matching, segment identification and toll calculation. A testing phase is taking place in early 2023, with final commissioning set for early 2025. Under the current Eurovignette scheme, HGV operators have to buy a small electronic device if they want to use motorways and toll highways in the Eurovignette countries, which include Denmark, Luxemburg, the Netherlands and Sweden. Eurovignette’s revenues are currently around €67 million per year. The Danish government expects the new GNSS-based scheme to match that until 2027, and then to double it from 2028 onwards.

The new scheme leverages significant technological advancements as well as increasing market maturity of GNSS telematics OBUs, mobile communications and enforcement equipment, all occurring during the past decade. The costs of establishing and operating the system will be comparable to those of previous non-GNSS-based schemes.

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The product gives access to the company’s global reference station network and L-band satellite distribution supplying corrections for GPS, Galileo, GLONASS and BeiDou.

The Vega 34 board connectors have no circuitry changes and are identical for all Vector users who can now add Atlas H10 and H30 PPP in their solutions. “Vega 34 gives our integrators an easy path forward to enrich their own product offerings,” said Miles Ware, Director of Marketing at Hemisphere. “They can take advantage of other standard features like over 1100 tracking channels, Cygnus interference mitigation technology and spectral analysis.” 

The Vega 34 uses dual antenna ports to create a series of additional capabilities including fast, high-accuracy heading over short baselines, RTK positioning, onboard Atlas L band, RTK-enabled heave, low-power consumption, and precise timing.

Scalable Solutions

With the Vega 34, positioning is scalable and field upgradeable with all Hemisphere software and service options. Utilize the same centimeter-level accuracy in either single-frequency mode, or employ the full performance and fast RTK initialization times over long distances with multi-frequency multi-constellation GNSS signals. High-accuracy L-band positioning from meter to sub-decimeter levels available via Atlas correction service.

Key Features

• Extremely accurate heading with long baselines
• Available multi-frequency position, dual-frequency heading supporting GPS, GLONASS, BeiDou, Galileo, QZSS, IRNSS, and L band (Atlas®)
• Atlas L band capable to 4 cm RMS
• Athena GNSS engine providing best-in-class RTK performance
• Excellent coasting performance
• 5 cm RMS RTK-enabled heave accuracy
• Strong multipath mitigation and interference rejection
• New multi-axis gyro and tilt sensor for reliable coverage during short GNSS outages

The introduction of the Vega 34 board brings a new firmware release. Version 6.05 extends several features and improvements and introduces NavIC (IRNSS) tracking and positioning across the Vega and Phantom product lines. Both RTK and Atlas positioning solutions are enhanced with an improved performance in challenging environments. Users of the BeiDou satellite systems and B2b PPP integrators will see significant advances in their solutions.

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In 2021, GNSS and EO downstream market generated over 200 billion euros revenues and are set to reach almost half a trillion over the next decade. EO and GNSS data have become increasingly important in the big data realm and paradigm responding to natural and man-made disasters, curbing the spread of disease and strengthening a global supply chain, among many other goals.

The Report provides analytical information on the dynamic GNSS and EO markets, along with in-depth analyses of the latest global trends and developments through illustrated examples and use cases. Using advanced econometric models, it also offers market evolution forecasts of GNSS shipments or EO revenues spanning to 2031.

With a focus on Galileo/EGNOS and Copernicus, the report highlights the essential role of space data across 17 market segments including,

• Agriculture; Aviation and Drones;
• Biodiversity, Ecosystems and Natural Capital;
• Climate Services; Consumer Solutions, Tourism, and Health;
• Emergency Management and Humanitarian Aid;
• Energy and Raw Materials; Environmental Monitoring;
• Fisheries and Aquaculture; Forestry;
• Infrastructure;
• Insurance and Finance;
• Maritime and Inland Waterways;
• Rail;
• • Road and Automotive;
• Urban Development and Cultural Heritage;
• and Space.

Some report highlights:

• Global annual GNSS receiver shipments will reach 2.5 bn units by 2031, dominated by the applications falling under the Consumer Solutions, Tourism and Health segment contributing roughly to 92% of global annual shipments;

• In EO, aside from the largest markets like Agriculture, Urban Development and Cultural Heritage or Energy and Raw Materials, the Insurance and Finance segment is expected to experience the fastest growth over the next decade (21 % of CAGR) for both EO data and value-added service revenues;

• From a supply perspective, the European Industry holds over 41% of the global EO downstream market and 25 % of the global GNSS downstream market.

“The flagship EU Space Programme, driven by Galileo and EGNOS on one side and Copernicus on the other, has become a major enabler in the downstream space application market. As a user-oriented agency, we provide this inside information on markets evolution to our users, being innovators, entrepreneurs, investors, academic researchers, chipset manufacturers, or simply anyone who looks into space to bring value to their activities. The added value and key differentiators of European GNSS and EO are showcased, both separately and in synergy with each other. I know that the report will be of great use and inspiration for those who are contributing to the EU economic growth,” concluded EUSPA Executive Director Rodrigo da Costa.

The report is available for download here.

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On general roads (approx. three meters wide), the module reliably enables vehicle positioning down to the lane level, according to the companies, as is required of vehicle-to-everything (V2X) applications for autonomous driving functions. The module’s target applications are telematics control units (TCU) and V2X onboard units (OBU).

The UMSZ6 Series GNSS Module uses a multi-frequency GNSS receiver chip based on Furuno’s Extended Carrier Aiding3 technology: the eRideOPUS 9 (model ePV9000B) and algorithm. Running costs associated with RTK4 base stations, correction data receiving, and correction data use are no longer needed, maximizing cost performance, while reliable vehicle positioning down to the lane level is possible even on general roads. Its dimensions of 17.8 × 18.0 × 3.11mm conform to automotive grade for customer freedom in end-product design.

“Relative vehicle positioning accuracy is constantly improving as a result of millimeter-wave radar, LiDAR and camera technology,” said Hideo Izumi, Vice President, Device Business, Alps Alpine Co., Ltd. “Achieving absolute position accuracy down to the lane level is essential for both V2X applications and genuine Level 3 automated driving, but system-related costs associated with RTK technology have been an obstacle. Getting around this with a multi-frequency GNSS receiver chip based on Furuno’s Extended Carrier Aiding technology, which realizes high-accuracy vehicle positioning to within 50 centimeters without correction data, will likely prove to be a breakthrough in V2X and advanced autonomous driving technology.”

The companies will evaluate the module’s performance through demonstration testing, targeting mass production in 2023.

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