Tag: GNSS chipsets

  • GNSS chipset shipments to hit 2.4B by 2029

    GNSS chipset shipments to hit 2.4B by 2029

    Demand for GNSS chipsets is rising globally, driven by growth in key verticals such as automotive and transportation, consumer electronics, and asset tracking applications. According to global technology intelligence firm ABI Research, global GNSS chipset shipments are projected to grow to 2.4 billion by 2029. 

    China is expected to lead this growth with the highest Compound Annual Growth Rate (CAGR) of 7.2% between 2024 and 2029, followed closely by Western Europe at 6.7% and the rest of the Asia-Pacific region at 5.6%. In contrast, the Middle East and Africa is anticipated to see slower growth, with a CAGR of 2.7%.

    “We are seeing a growing demand for consumer electronics, especially in countries like India, Indonesia, Vietnam and Thailand,” explains Rachel Kong, research analyst at ABI Research. “This is fueled by the rapidly growing middle-class populations and smartphone penetration rates, which are boosting the adoption of GNSS-enabled smartphones, wearables and tablets. In particular, the Sport & Wearables category – which includes devices such as smartwatches, smart glasses, fitness and wellness trackers, and wearable cameras – is forecast to see the highest CAGR of 13.2% between 2024 and 2029.”

    Increased E6 band support

    Another key technology experiencing rapid growth is GNSS chipsets supporting the E6 band, an emerging GNSS frequency designed for high-precision applications. These chipsets are expected to achieve a strong CAGR of 36.7% between 2024 and 2029, largely driven by high-precision applications such as autonomous driving, aerospace, critical infrastructure monitoring, land surveying, and new location-based services.

    With increasing global interoperability and integration of multiple GNSS solutions, worldwide demand for seamless, high-precision navigation continues to rise.

    “Manufacturers are more willing to adopt multi-constellation chipsets to support a broader range of applications and geographies,” said Kong.

    In addition, vendors such as Quectel, Unicore, Trimble and ComNav Technology are increasingly launching and developing products that support the E6 band. Recent updates, including Trimble’s firmware enabling Galileo High Accuracy Service (HAS) on its devices, and Unicore introducing the UM981 high-precision positioning model, demonstrate the growing traction of this frequency band. Multi-frequency bands are also gaining significance by offering enhanced positioning accuracy, improved signal reliability, and better resistance to interference.

    “These bands are already widely used in sectors such as aviation, maritime and automotive, and their adoption will continue to grow as new use applications emerge and evolve over time,” Kong said.

    These findings are from ABI Research’s Outdoor/Wide Area Location Technologies market data report, part of the company’s Space Technologies and Innovation research service, which includes research, data and ABI Insights.

  • RX Networks collaborates with Qualcomm to provide smartphone location accuracy

    RX Networks collaborates with Qualcomm to provide smartphone location accuracy

    Logo: Rx NetworksRx Networks, Inc., a GNSS data services company, announced the availability of TruePoint.io precise location services on Qualcomm’s Snapdragon 8 Gen 1 and Snapdragon 888 5G Mobile Platforms. TruePoint.io integration empowers Android smartphones to achieve enhanced location accuracy down to a meter or less – something previously only seen with high-grade receivers.

    With enhanced location accuracy, superior user experience for such use cases as rideshare, micro-mobility, health and fitness and lane-level requirement applications can now be realized. The enablement of reliable meter-level location accuracy on mobile phones will unlock the potential of location-based services and open the door for other innovative and unique use cases. The limitations of a standalone GNSS chipset no longer become the barrier to pursuing the vision of connected ecosystems reliant on location.

    TruePoint.io enables scalable, reliable, and affordable ways to leverage high-precision location on smartphones powered by Snapdragon mobile platforms. Rx Networks’ global coverage, including China, gives smartphone OEMs the advantage of a single GNSS corrections vendor that works across all continents.

    “Rx Networks provision of GNSS data services for accurately positioning smartphones using Snapdragon mobile platforms will enable meter-level location accuracy almost everywhere smartphones can connect to a terrestrial cellular network,” said Francesco Grilli, Vice President, Product Management at Qualcomm Technologies, Inc. “Meter-level location accuracy is poised to improve smartphone user experiences and spur the creation of exciting and innovative services for businesses and consumers.”

    TruePoint.io is scheduled to be available on Snapdragon mobile platforms initially in China in Q4 2022 and globally in H1 2023.

  • Swift Navigation honored with fleet management award

    Swift Navigation honored with fleet management award

    Swift Navigation logoSwift Navigation has been named Fleet Management Technology Company of the Year in the second annual AutoTech Breakthrough Awards conducted by AutoTech Breakthrough.

    AutoTech Breakthrough is a market intelligence organization that recognizes the top companies, technologies and products in the global automotive and transportation technology markets.

    Swift offers a highly-accurate, highly-reliable precise positioning solution that improves the operational efficiency of commercial transport, long-haul trucking and last-mile delivery, whether human-driven or autonomous. Swift’s fleet management precise positioning solution is comprised of the Skylark precise positioning service—delivering continent-wide, cloud-based corrections service — and the receiver-agnostic Starling positioning engine, which works with a variety of automotive-grade GNSS chipsets and inertial sensors, making centimeter-level GNSS accuracy a possibility without the cost of all new equipment.

    Swift’s precise positioning solution delivers improved GNSS accuracy to make it easier to enable key fleet management capabilities such as lane-level analytics, route optimization and accurate traffic flow analytics to improve operational efficiency.

  • Rx Networks TruePoint.io global PPP corrections now quad-constellation

    Rx Networks TruePoint.io global PPP corrections now quad-constellation

    Logo: Rx NetworksRx Networks TruePoint.io global precise point positioning (PPP) correction service now provides quad-constellation support.

    More mobile devices are integrating multi-constellation GNSS chipsets for better positioning. With quad-constellation expanded multi-constellation support, Rx Networks TruePoint.io global precise point positioning (PPP) correction service unlocks that accuracy, providing global PPP corrections for every major GNSS constellation those chips can track.

    TruePoint.io global PPP originally delivered GPS and GLONASS corrections. Now, it also provides corrections for Galileo and BeiDou. Mass-market multi-constellation GNSS chipsets can now augment all their satellite measurements with accuracy and fully leverage their positioning capabilities with quad-constellation support.

    Consumer devices now have the potential to achieve 50-cm position accuracy when using Rx Networks services for any of the four GNSS constellations. Other internet of things (IoT) and infrastructure applications that do not require real-time positioning can realize 10-cm accuracy in a variety of environments.

    Multi-constellation correction capability ushers in new possibilities and use cases for the connected receiver, according to Rx Networks.

    TruePoint.io remains ubiquitous and as flexible as possible to GNSS chipsets using industry standard formats, and is also receptive to custom integration services for unique usage scenarios. By offering PPP and other high accuracy services in a variety of data standards, TruePoint.io empowers telecom providers with a straightforward approach to integrating high- accuracy services that provide more value to their client devices, and propels the development of exciting new use cases.

    “With this new expansion of TruePoint.io, applications already serviced by Rx Networks can accelerate their market growth objectives with better accuracy and precision using constellations ideal for target regions,” said Vincent Chen, product manager of Truepoint.io. “Being able to deliver global PPP corrections for GPS, GLONASS, Galileo and BeiDou also sets the stage for the addition of more constellations like QZSS. Stay tuned.”

  • GSA releases 3rd GNSS User Technology Report

    GSA releases 3rd GNSS User Technology Report

    Report cover GSA
    The full GNSS User Technology Report 2020 is available for download. (Cover: GSA)

    News from the European GNSS Agency

    The European GNSS Agency (GSA) has released its latest GNSS User Technology Report, providing a comprehensive analysis of GNSS trends and developments.

    With four GNSS available and more than 100 satellites in operation broadcasting multiple frequencies, the GNSS industry is shifting towards the wide adoption of multifrequency receivers across market segments to meet the diverging user needs of emerging applications.

    The report includes contributions from leading GNSS receiver, chipset manufacturers and service providers, and serves as a valuable tool to support planning and decision-making with regards to developing, purchasing and using GNSS technology.

    Published biennially since 2016, the User Technology Report has become a point of reference for the GNSS industry, research and policy-makers.

    Rapid Evolution

    ‘’The GNSS industry is evolving at a rapid pace and is shaped by the dynamics of emerging applications and user needs as well as the upgrade of existing and new GNSS and Satellite Based Augmentation Systems (SBAS),” said Rodrigo da Costa, GSA executive director. “The industry has understood the potential of Galileo’s unique features.”

    The third edition of the report begins with a chapter devoted to technology trends common to all segments: receiver design, position processing and signal processing. It also discusses protection measures against GNSS jamming and spoofing, such as authentication, including what 5G and other technologies and sensors can do, in combination with GNSS.

    With multi-constellation now being the norm, the industry is moving towards the wide adoption of multi-frequency receivers even for usually power- and cost-constrained consumer solutions. The Galileo E5 is becoming the preferred frequency with about 20% of all receiver models in the market already using it.

    The report is built around four macro segments defined on the basis of commonalities from a technology point of view:

    • high volume
    • safety- and liability-critical
    • high-accuracy
    • timing devices and solutions (a new-entry in this edition)

    Each chapter starts with the macrosegment characteristics and receiver capabilities, depicts the industry landscape and typical receiver form factor, it then delves into the key current and future drivers and trends, and finishes with the added value of the EGNSS, Galileo and EGNOS, for the macrosegment at stake.

    Space Data for Europe

    This year editor’s special “Space Data for Europe” sheds light on the role that Copernicus and Galileo play within the European Space Programme in the data management and use, now and in the future. It also provides a vision of major transformations underway within our society and our economy and the benefits expected from this digital transformation, paving the way towards the European Data Strategy and Green Deal.

    “Today, Galileo and EGNOS already provide increased capabilities which are being used across a broad range of applications, and are already igniting the next generation of location-based applications. In the future, new services — the Galileo High Accuracy Service (HAS), Galileo Open Service Navigation Message Authentication (OS-NMA) and Commercial Augmentation Service (CAS) — will raise the accuracy and reliability bar even higher, and dramatically enhance positioning, navigation and timing solutions for businesses and citizens.

    By bringing insight and understanding into the evolutions of GNSS technology, we are creating opportunities for innovation,” concluded da Costa.

    The full GNSS User Technology Report 2020 is available for download.

  • Innovation: Multi-band GNSS with embedded functional safety for the automotive market

    Innovation: Multi-band GNSS with embedded functional safety for the automotive market

    Autonomous Driving Guidance

    GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving. This month, we look at a development with embedded functional safety.

    By Fabio Pisoni, Domenico Di Grazia, Giuseppe Avellone, Luis Serrano, Brett Kruger, Laura Norman and Natasha Wong Ken

    INNOVATION INSIGHTS by Richard Langley
    INNOVATION INSIGHTS by Richard Langley

    I DRIVE A 10-YEAR OLD KIA SPORTAGE. It is still quite roadworthy despite having to contend with New Brunswick winters. However, it lacks some of the safety features that are present in newer cars. There is no back-up camera, no forward-collision warning, no automatic emergency braking, and no blind-spot warning, for example. These are just some of the safety systems that come as standard or optional on most new cars these days. Still, the driver is responsible for the safety and operation of the car at all times. True, help might be provided for parallel parking and cruise control, but that’s about it for automated operation with most vehicles.

    But things are changing and changing fast. Real automation is coming to automobiles. Already partial automation is available in some high-end vehicles that can take over steering, braking and acceleration in certain circumstances. The driver is still responsible for other aspects of the vehicle’s operation including paying attention to road conditions. Soon, we will have conditional automation where the car can drive itself but the driver must stay alert and be prepared to take over immediately at any time. Next will come high automation where a computer fully drives the car at certain times on certain routes such as a highway. Multiple systems, including back-up systems, will maintain a required safety level and the car will determine if it is safe to operate autonomously. If not, it could pull over to the side of the road and shut down. And finally, we may have full automation of cars. They will be able to drive on any road under virtually any conditions and won’t need any controls such as steering wheels or accelerator or brake pedals.

    Augmented GNSS guidance will play a major role in the automation of vehicles. As with any navigation or guidance system, there are four important requirements: accuracy, availability, continuity and integrity. Perhaps the most obvious requirement, accuracy describes how well a measured value agrees with a reference value, typically the true value. How well a system accounts for various errors or biases determines the accuracy of corrected measurements and, ultimately, the accuracy of a derived position. A navigation system’s availability refers to its ability to provide the required function and performance within the specified coverage area at the start of an intended operation. In many cases, system availability implies signal availability. Environmental factors such as signal attenuation or blockage or the presence of interfering signals might affect availability. Ideally, any navigation system should be continuously available to users. But, because of scheduled maintenance or unpredictable outages, a particular system may be unavailable at a certain time. Continuity, accordingly, is the ability of a navigation system to function without interruption during an intended period of operation.

    While accuracy, availability and continuity of a guidance system are all important, it is the integrity or trustworthiness of the system that is paramount. It is why the automotive industry has already developed integrity standards for the automation of vehicles. And it is why GNSS chip manufacturers and positioning systems developers are working on bespoke devices for autonomous driving, whatever the level of automation. In the Innovation column this time around, we’ll learn about one such development — one with embedded functional safety.


    Autonomous driving applications are raising the requirements for onboard GNSS receivers to new highs. Position accuracy, protection levels, high availability, robustness of operation and integrity are the priorities shaping a new class of automotive components and architectures. Autonomous driving deals with life-critical issues: the expectation of reliability and safety for this new generation of receivers, as well as for other sensors and systems, is very high.

    The International Organization for Standardization (known by the language-independent short form ISO) has issued documents codifying functional safety (FuSa) for automotive applications: ISO 26262: part 1 to part 11. ISO 26262 complements the well-known automotive reliability standard published by the Automotive Electronics Council, AEC-Q100. With respect to FuSa, a system can be defined as functionally safe if it always operates correctly and predictably. More importantly, in the event of failures, the system must remain safe for people. Lastly, as security is becoming paramount, a new standard for cybersecurity in automotive applications — ISO/SAE 21434 — is in development by ISO and SAE International (initially called the Society of Automotive Engineers) that will require a GNSS receiver to be robust against jamming, spoofing and meaconing attacks.

    The Automotive Safety Integrity Level (ASIL) is a key part of ISO 26262 compliance, and the standard specifically identifies the minimum testing requirements depending on the ASIL of the component. The ASIL of a component or system depends on the ASIL of the target application. The ASIL is determined at the beginning of a development process. It varies from ASIL-A to ASIL-D, where A is for less critical applications and D for the most critical ones such as steering and breaking systems. ASIL-rated lane-level positioning performance can be demonstrated today by combining an ASIL-B software positioning engine and TerraStar-X correction technology from Hexagon Positioning Intelligence with GNSS measurements from an ASIL-B-rated GNSS chipset.

    To conjugate performance requirements with the demand of embedded functional safety, STMicroelectronics has developed TeseoAPP (STA9100), a next-generation GNSS component, designed to meet an ASIL-B level of safety. TeseoAPP is a multi-band GNSS measurement engine. It outputs all the observables, navigation and integrity data required by a safety-critical precise positioning algorithm, located on a host processor. TeseoAPP also computes a local L1 code-based standard position, velocity and time (PVT) solution (SPS) for monitoring and integrity purposes. Also part of the baseline features are autonomous satellite acquisition (cold start condition), real-time assistance, data decoding and storage on external non-volatile memory (NVM), accurate timing and pulse-per-second generation under vehicle dynamics.

    RECEIVER ARCHITECTURE

    The target architecture for a safety-critical platform is sketched in FIGURE 1, where a host microprocessor is in charge of collecting GNSS observables and sensor data from the TeseoAPP. The latter includes on the same chip die a first configurable RF chain for the L1 signal ensemble and the baseband part for processing all the signals in the served bands, while the second chip is an RF front end (code-named STA5635), configurable for receiving the other served bands (such as GPS L2 or L5, Galileo E5a or E5b or E6, and so forth). The two chips are clearly visible in the photograph of a TeseoAPP evaluation module of FIGURE 2.

    FIGURE 1. Block diagram of the TeseoAPP platform for safety-critical applications, featuring surface-acoustic-wave (SAW) filters, a temperature-compensated crystal oscillator (TXCO), non-volatile memory (NVM) and both internal and external STA5635 tuners. (See text for other initialisms used.) Diagram: Authors)
    FIGURE 1. Block diagram of the TeseoAPP platform for safety-critical applications, featuring surface-acoustic-wave (SAW) filters, a temperature-compensated crystal oscillator (TXCO), non-volatile memory (NVM) and both internal and external STA5635 tuners. (See text for other initialisms used.) Diagram: Authors)

    FIGURE 2 The TeseoAPP Evaluation Module, including the STA9100 (TeseoAPP) and STA5635 (external tuner). Photo: Authors
    FIGURE 2 The TeseoAPP Evaluation Module, including the STA9100 (TeseoAPP) and STA5635 (external tuner). Photo: Authors

    The selected frequency plan and constellation configuration depend on the specific autonomous driving scenario and the target geographic area. The TeseoAPP supports a mix of frequencies and signals as shown in TABLE 1. The chipset baseband unit can track up to 80 channels. A tracking snapshot from a rooftop antenna (located at the ST office in Naples, Italy) is illustrated in FIGURE 3.

    Both the TeseoAPP and the STA5635 have been designed for ASIL-B following the concept of “safety element out of context” (SEooC) described in ISO standard ISO 26262:2012. In this context, assumptions have been made for the application (such as on the mission profile), identifying the related safety goals from which functional and technical safety requirements have been derived.

    TABLE 1. The TeseoAPP (STA5635) supported frequency plans and scenarios.
    TABLE 1. The TeseoAPP (STA5635) supported frequency plans and scenarios.

    FIGURE 3 Screenshot of the L1-L5 TeseoAPP configuration, from the ST Teseo-Suite tool (using the Naples rooftop antenna). Image: Authors
    FIGURE 3. Screenshot of the L1-L5 TeseoAPP configuration, from the ST Teseo-Suite tool (using the Naples rooftop antenna). Image: Authors

    Following the guidelines identified in the ISO 26262 flow for safety-relevant product development, several safety mechanisms have been identified at the hardware, firmware and system/boot level. The microcontroller unit (MCU) supports dual-core operation in a lock-step configuration to verify processor output errors together with a memory built-in self-test (executed at startup) and error correction code on a safety-related embedded random access memory. Other hardware redundancies have been introduced in safety relevant parts such as triple-voted registers for critical configuration parameters. For the real-time operating system (RTOS), an ASIL-D-level product — the highest level — was selected.  Functional safety analysis of the GNSS sub-system has produced a dedicated technical safety concept, including aspects such as tuner operation, interference and jamming mitigation, signals and observables quality management (QM), reliable host communication (using generic end-to-end or E2E protocols for data integrity and resilient flow control), and reliable system software. A simplified overview of all these safety mechanisms is outlined in FIGURE 4, where the orange-colored blocks are specific for the GNSS sub-system.

    FIGURE 4. Overview of the TeseoAPP safety mechanisms. (See text for acronyms and initialisms used.) Diagram: Authors
    FIGURE 4. Overview of the TeseoAPP safety mechanisms. (See text for acronyms and initialisms used.) Diagram: Authors

    Safety Mechanisms. The technical safety concept of the GNSS sub-system is implemented by a security, integrity and safety (SIS) monitoring layer (see FIGURE 5). The SIS collects information and metrics from other receiver blocks embedded in the RF/baseband hardware and from different components of the GNSS firmware stack. The SIS internally computes integrity risk estimates, which are delivered to a central intelligence monitor (CIM) capable of switching the receiver into a safe state, within a fault-tolerant time interval, when the overall receiver integrity appears compromised. In its simplest form, the CIM can be represented by a weighted sum of integrity risk inputs, followed by some activation function. During this process, a first layer of logic (CIM-L1) combines a subset of signal quality metrics to decide a priori which observables shall be passed to the host or discarded (not delivered).

    FIGURE 5 Safety information flow through the TeseoAPP security, integrity and safety layer. (IP = intellectual property; other short forms in text.) Diagram: Authors
    FIGURE 5 Safety information flow through the TeseoAPP security, integrity and safety layer. (IP = intellectual property; other short forms in text.) Diagram: Authors

    The collected signal metrics include quality estimators (based on multi-correlation techniques for example) or classic linear combinations of observables (such as dual-frequency carrier-phase differences or code-minus-carrier). Receiver metrics, on the other hand, have a more global scope and include estimators for inter-frequency biases, system-time cross-checks among constellations, and so on. The fault collection and control unit (FCCU) conveys hardware status flags to the SIS. Typically, an FCCU exception indicates some critical hardware failure and takes a priority path when switching the safe state. For example, a fault in the MCU lock-step monitor will trigger an immediate firmware action, mediated by the FCCU.

    POSITIONING PERFORMANCE

    To demonstrate the performance that can be achieved using the ST TeseoAPP chipset, Hexagon Positioning Intelligence (PI) has combined measurements from the TeseoAPP with an automotive-grade antenna and Terrastar-X correction technology, and processed the data using Hexagon PI’s software positioning engine. Even with a modern receiver supporting dual-frequency, multi-constellation measurements, such as the TeseoAPP, corrections are necessary to deliver decimeter-level performance and safety information required by an autonomous vehicle.

    In clear-sky environments, lane-level positioning accuracy is achieved, enabling GNSS as a key input to autonomous systems. FIGURE 6 shows the horizontal error performance of the combined ST+PI solution in the form of an error time series and an error cumulative distribution function (CDF). The error performance expected from today’s single frequency automotive-grade GNSS without corrections and processing is also shown for comparison.

    FIGURE 6. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone and of the TeseoAPP with PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
    FIGURE 6. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)

    For guidance systems in autonomous applications, the GNSS position must be accompanied by safety information and integrity guarantees. The concept of protection levels (PLs) has been introduced to provide this. A horizontal protection level defines a circle or ellipse around the reported GNSS position, which will have some error, within which the actual position is guaranteed to fall. The Hexagon PI software positioning engine is ASIL-B rated, so its position and PL outputs are available for use in safety-related autonomous applications. The autonomous system using the GNSS position is assured that its actual position is within the protection level ellipse. To output ASIL-B-rated positions accompanied by PLs, ASIL-rated GNSS measurement inputs are required.

    Using the inputs and techniques described above, the Hexagon PI software positioning engine calculates PLs for every GNSS position output. The Hexagon PI data from Figure 6 is shown again in FIGURE 7 with accompanying PL information. In this case, a PL with integrity risk of 10-7 is shown, meaning that the actual position error is expected to exceed the reported PL at a rate less than 10-7 per hour.

    FIGURE 7 Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)
    FIGURE 7. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in an open-sky environment. (Image: Authors)

    The PLs shown in Figure 7 are typically much greater than the position error. This is because the protection level calculation must account for a large number of potential faults that are not generally present. For instance, undetectable GNSS satellite faults can occur at rates greater than 10-7 per hour, and so must be accounted for in the PL.

    In non-clear-sky environments, the GNSS position calculation is complicated by frequent loss of “sight” of the GNSS satellites. This is mitigated by having additional constellations and frequencies. However, for added availability of a precise position in challenging environments, it is necessary to incorporate sensor fusion into the position calculation, typically by using a six degree-of-freedom inertial measurement unit (IMU) as input, which includes three accelerometers and three gyroscopes to measure 3D translational and rotational motion. The IMU can maintain position accuracy for short periods when GNSS is unavailable, such as when driving under an overpass on a highway. The IMU provides a relative positioning output, so the absolute error growth is unconstrained in the absence of GNSS inputs. Therefore, it is important to have the GNSS receiver as the primary sensor in the positioning solution to constrain IMU drift and to reacquire GNSS signals rapidly after emerging from a GNSS outage.

    Position error results for a typical highway environment are shown in FIGURE 8 after adding input from an automotive-quality IMU to the Hexagon PI software positioning engine. Small spikes in position error are due to short GNSS outages along the test route. However, the error growth due to loss of GNSS is minimal due to the coupling of the IMU data with the GNSS measurements.

    FIGURE 8 Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone, and of the TeseoAPP with PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
    FIGURE 8. Horizontal error time series and cumulative distribution function (CDF) of the TeseoAPP alone, and of the TeseoAPP with Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)

    FIGURE 9 shows the Hexagon PI highway data with accompanying PLs. Though the errors are well-constrained through GNSS outages, the PLs typically increase significantly. This is due to the higher noise of low-cost IMUs, and the uncertainty associated with reacquiring GNSS signals. PLs must account for worst-case IMU performance, which can have errors orders of magnitude greater than the nominal performance. During GNSS signal reacquisition, minimizing receiver noise is critical for fast position reconvergence, reinforcing the need for high-quality GNSS in autonomous applications.

    FIGURE 9. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the PI software positioning engine (SWPE) in a highway environment. (Image: Authors)
    FIGURE 9. Horizontal error and protection level (PL) including cumulative distribution functions (CDFs) of the Hexagon PI software positioning engine (SWPE) in a highway environment. (Image: Authors)

    CONCLUSION

    The TeseoAPP is the first generation of multi-band GNSS chipsets designed by STMicroelectronics to meet the two main requirements of autonomous driving: accuracy and safety-critical operation. The execution of the ISO 26262 standard for TeseoAPP is still a work in progress and encompasses two main aspects: 1) a safety plan implementation, code quality metrics and processes management and 2) the technical safety concept. Both of these aspects presented specific challenges, mainly due to the inherent complexity of the product and the large amount of firmware involved.

    To exploit the maximum benefit of the TeseoAPP in safety-critical automotive applications, a high-accuracy ASIL-B-rated position engine is required. Hexagon PI’s software positioning engine is designed to use measurements from an ASIL-rated GNSS receiver, along with GNSS corrections and IMU data, to generate ASIL-rated position outputs, with accompanying integrity guarantees. The Hexagon PI software positioning engine computes protection levels. The calculation and determination of PLs is required to meet the safety and integrity guarantees necessary in autonomous driving for functionally safe operation.  The software positioning engine also outputs ASIL-rated velocity, attitude and absolute time data, although we have not discussed these in this article.

    The required high performance and safety expectations suggested, since the early stages of the project, a system composition in which the TeseoAPP was configured as an ASIL-B measurement-engine whereas the ASIL-B software positioning engine algorithms (by Hexagon PI) run on a separate ASIL host processor. We believe this synergy of competencies will represent the key for a successful solution to enable safe and reliable positioning in autonomous driving applications.

    ACKNOWLEDGMENTS

    The TeseoAPP chipset has been developed with the support and in the framework of the European Safety Critical Applications Positioning Engine project, which is funded by the European GNSS Agency under the European Union’s Fundamental Elements research and development program.


    FABIO PISONI leads the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Milan, where he has worked since 2009. He has a degree in electronics from Politecnico di Milano and has previous experience as a GNSS and digital signal processing (DSP) engineer.

    DOMENICO DI GRAZIA is a GNSS signal senior staff engineer at STMicroelectronics Italy in Naples, where he has worked since 2003. He has a degree in telecommunication engineering from the University of Naples Federico II, holds patents in the GNSS area, and has previous experience in digital communications.

    GIUSEPPE AVELLONE is in the GNSS System Architecture and Software Team (Automotive and Discrete Group) at STMicroelectonics Italy in Catania, where he has worked since 1998. He has a degree in electronics from Università di Palermo and previous experience as a GNSS and DSP engineer.

    LUIS SERRANO is a GNSS technical marketing manager with STMicroelectronics based in Munich. He holds a Ph.D. in GNSS from the Department of Geodesy and Geomatics Engineering, University of New Brunswick, Canada. He has been active in the GNSS precise positioning field since 2007, and holds a patent on GNSS.

    BRETT KRUGER is a software engineer specializing in GNSS/INS integration in the Safety Critical Systems Group at the Hexagon Positioning Intelligence (PI) NovAtel brand  in Calgary, Canada. He holds an M.A.Sc. in electrical engineering from the University of Toronto, Canada.

    LAURA NORMAN is a geomatics engineer specializing in GNSS integrity and protection levels in Hexagon PI’s Safety Critical Systems Group. She obtained her M.Sc. from the Department of Geomatics Engineering at the University of Calgary, Canada.

    NATASHA WONG KEN is the Safety Critical Systems product manager at Hexagon PI. She has worked at Hexagon PI since 2012 after obtaining a B.Sc. in geomatics engineering from the University of Calgary.


    FURTHER READING

    • Standards for Vehicle Safety

    Keeping Safe on the Roads: Series of Standards for Vehicle Electronics Functional Safety Just Updated” by C. Naden, ISO, 19 Dec. 2018.

    Road vehicles – Functional safety, ISO 26262:2018 (parts 1 to 12), International Organization of Standardization, Geneva, Switzerland, December 2018.

    Failure Mechanism Based Stress Test Qualification for Integrated Circuits, AEC – Q100 – Rev-H, Automotive Electronics Council, 11 Sept. 2014.

    • STMicroelectronics TeseoAPP (STA9100)

    STA9100MGA, Automotive TeseoAPP (ASIL Precise Positioning) Family Multi Band GNSS Precise Measurement Engine Receiver, DB3546, Data Brief, STMicroelectronics, Geneva, Switzerland, 26 Feb. 2018.

    • Future GNSS Automotive Positioning

    NovAtel Pioneers Autonomous Solutions with Positioning Engine, Corrections Services, Integrity Research” by T. Cozzens in GPS World, Vol. 29, No. 5, May 2018, pp. 33–34.

    Lane-level Positioning with Low-cost Map-aided GNSS/MEMS IMU Integration” by M. M. Atia and A. Hilal in GPS World, Vol. 29, No. 5, May 2018, pp. 18–32.

    Quo Vademus: Future Automotive GNSS Positioning in Urban Scenarios” by M. Escher, M. Stanisak and U. Bestmann in GPS World, Vol. 27, No. 5, May 2016, pp. 46–52.

    • Precise Point Positioning

    Two Are Better Than One: Multi-frequency Precise Point Positioning Using GPS and Galileo” by F. Basile, T. Moore, C. Hill, G. McGraw and A. Johnson in GPS World, Vol. 29, No. 10, October 2018, pp. 27–37.

    More Is Better: Instantaneous Centimeter-level Multi-frequency Precise Point Positioning” by D. Laurichesse and S. Banville in GPS World, Vol. 29, No. 7, July 2018, pp. 42–47.

    Where Are We Now, and Where Are We Going? Examining Precise Point Positioning Now and in the Future” by S. Bisnath, J. Aggrey, G. Seepersad and M. Gill in GPS World, Vol. 29, No. 3, March 2018, pp. 41–48.

    • Integrity of Automobile Positioning

    Expert Opinions: Integrity in the Vehicle Environment. Question: Why do we need to take integrity seriously in the vehicle environment?” by C. Rizos, R. Bryant and S. Pullen in GPS World, Vol. 28, No. 1, January 2017, p. 8.

    Integrity for Non-Aviation Users: Moving Away from Specific Risk” by S. Pullen, T. Walter and P. Enge in GPS World, Vol. 22, No. 7, July 2011, pp. 28–36.

    The Integrity of GPS” by R.B. Langley in GPS World, Vol. 10, No. 3, March 1999, pp. 60–63.

  • Allystar releases multi-band GNSS raw data chip and module

    Allystar releases multi-band GNSS raw data chip and module

    Allystar Technology Co. Ltd., headquartered in Shenzhen, China, has released a multi-band multi-GNSS chipset, the HD9310. The new product is based on the Cynosure III architecture integrating multi-band multi-system GNSS RF and baseband.

    A multi-band, multi-system system-on-chip, it supports BeiDou-3 and is capable of tracking all global civil navigation systems (GPS, BeiDou, Galileo, GLONASS, IRNSS, QZSS and SBAS) in all bands (L1, L2, L5, L6), said Simon Sun, Allystar general manager.

    Photo: Allystar Technology
    Photo: Allystar Technology

    Designed for high-precision applications, the HD9310 measures 5.0mm x 5.0mm. The architecture integrates floating-point arithmetic units based on ARM CortexM4, 160 KB RAM, 32KB backup RAM with VBAT, 386 KB embedded FLASH and peripheral interfaces UART, I2C, SPI, GPIO, CAN.

    In terms of the manufacturing processes, it adopts a 40nm process and incorporates a variety of advanced design technologies, endowing it with very power consumption: less than 50mA.

    The quad-flat no-leads package allows customers to reduce printed circuit board and bill of materials costs while reducing the number of peripheral devices. This chip supports CAN interface and can be widely used in vehicle management, car navigation, wearable devices, GIS data collection, precision agriculture, smart logistics, driverless, engineering survey and other fields.

    “The HD9310 supports three options of RF setting — A, B, C — for product developers to quickly bring their ideas to the different application and markets,” added Shi Xian Yang, high precision project manager at Allystar.

    Three available options for the HD9310 chipset. Graphic: Allystar Technology
    Three available options for the HD9310 chipset. Graphic: Allystar Technology

    • Option A, focused on L5 band, L5/E5, maximizes measurement accuracy and improves multipath mitigation based on higher chip rate.
    • Option B is focused on L2 band, and suitable for relative position applications, for example, real-time kinematic (RTK), because worldwide continuously operating reference stations (CORS) commonly support L1/L2/L1OF/L2OF.
    • Option C is focused on the L6 band and is designed for PPP applications, receiving state space representation (SSR)-type corrections to be broadcast from satellites in the coming future, and supporting B3I already.

    The HD9310 comes with built-in support for standard RTCM Protocol (MSM), supporting multi-band multi-system high-precision raw data output, including pseudo range, phase range, Doppler, SNR for any kind of 3rd party integration and application.

    Module.  Allystar Technology also has launched a multi-band multi-GNSS module, TAU1302, which integrates the HD9310 chipset and measures 12 × 16 × 2.3 millimeters.

    With the features of small size, low power consumption (<50 mA), and ease of integration and mass production, HD9310 is suitable for high-precision applications such as vehicle management, car navigation, wearable devices, GIS data collection, precision agriculture, smart logistics, driverless, engineering survey and other fields.

    Customer samples of the HD9310 chipset are available now.

  • Positioning with Android: GNSS observables

    Positioning with Android: GNSS observables

    (Image: Authors/Trimble)
    (Image: Authors/Trimble)

    For those who want high accuracy, but don’t need it full time, high-productivity dedicated professional solutions may not be cost-justified. In these cases, a “positioning as a service” subscription could offer a viable use model.

    Achieving precision positioning with just a standard mobile device, a correction stream using the mobile device’s data connection and a high-accuracy positioning application produces a very low barrier to achieving high accuracy.

    By Stuart Riley, Herbert Landau, Victor Gomez, Nataliya Mishukova, Will Lentz and Adam Clare, Trimble Inc.

    We expect that for professional applications that need precision positions, a dedicated system that employs a custom GNSS chipset and purpose-built applications will continue to be the right solution. However, it becomes clear that the ubiquity of consumer mobile devices, with increasing computing power, ruggedness and an expanding feature set, presents fertile ground for new development of improved positioning systems that don’t have strict professional requirements.

    A range of new use models and applications will be enabled by consumer mobile phones with technology that improves positioning performance. The goal of the work presented here is to assess what level of performance can be achieved by using proprietary PVT (position, velocity, time) engines utilizing GNSS measurements from the Android GNSS measurement application programming interface (API).

    We first review GNSS measurement and positioning performance from a subset of the current Android phones/tablets currently on the market. Then we show the position performance achievable using precision engine with measurements from a dual-frequency GNSS chipset targeted for the cellular handset market. This class of device is expected to be integrated into consumer cellular devices on the market within the next 1 to 2 years.

    Performance of Current Phones

    We tested various devices including the Nexus 9 (which provides phase data) and various other Android devices that implement the new API. Most devices tested do not support phase data; of the few devices tested that do provide phase data, all except the Nexus 9 implement GNSS power duty cycling. This is a mode where the GNSS chipset is only active for a fraction of each second to reduce power consumption. This results in cycle slips each epoch, which makes carrier-phase processing for real-time kinematic (RTK) unusable.

    During the testing a wide range of performance across devices was observed. Figure 1 shows the C/NO for a high-elevation GPS satellite collected at the same time from two different Android models that implement the GNSS measurement API. The units were located in a clear environment less than a meter apart. Deep fades are present, most likely caused by deconstructive multipath.

    Figure 1. Comparison of the C/NO from two different Android devices.

    However, the devices show significantly different tracking performance: device B reports over 10 dB lower C/NO for much of the test and eventually stops reporting measurements. During our analysis, around six different Android devices have been tested; it isn’t clear whether the devices tested are typical over a broader population of device types.

    Before attempting to position with observables from Android devices the measurement quality was analyzed. As only a subset of current devices that support the API provide phase information we wanted to evaluate both a phase-based RTK engine and a pseudorange/Doppler based code engine to determine what is possible from each class of device.

    One of the devices tested was a Samsung S7 device. It provides pseudorange, Doppler and phase via the GNSS measurement API. However, the phone implements power duty cycling so after a short period of operation the duty cycling mode was enabled which resulted in a cycle slip on the phase every epoch.

    To derive an improved position from this class of device pseudorange and Doppler can be fed into a code-phase positioning engine. Fortunately, the Doppler provided by the device is of reasonable quality as can be seen from Figure 2.

    Figure 2. Android GNSS observables: Doppler versus time-differenced pseudorange.

    In this simple analysis measurements from a single high elevation satellite were analyzed. The Doppler is plotted along with the differenced pseudorange converted into L1 cycles. It can be seen that as expected the Doppler has much lower noise and so can be used in a pseudorange smoother.

    A simple way to view the pseudorange noise is to subtract the carrier phase from the pseudorange. If there are no cycle slips this should show ionospheric divergence with the noise dominated by the pseudorange noise. The absolute level is arbitrary as it includes integer carrier cycles. Figure 3 shows an example from an Android device.

    Figure 3. Android GNSS observables: pseudorange — carrier phase.

    The data was captured on a building roof in an open environment. There’s a slight downward trend due to the ionospheric divergence between code and carrier, but the metric is dominated by the pseudorange noise. For this example from a high elevation GPS satellite the standard deviation is 6.5 meters. For comparison, a precision receiver connected to a precision GNSS antenna providing unsmoothed pseudorange in this environment would have a standard deviation of a few decimeters.

    Another way to assess the measurement performance is to form double difference residuals. Data was logged from pairs of identical devices mounted with a common orientation. An RTK system was used to measure the same point on each device. The camera lens location above the screen was used as the reference point.

    An accurate vector between the two references points was computed and used as truth in a double-difference residual analysis. Even though we do not know the precise location of the phase center of the antenna, because the difference was performed between two devices that are the same model and have the same orientation the error in the phase center location is common and will cancel. Various pairs of devices were tested by being mounted on a wooden board on a tripod at approximately waist height. The test configuration is shown in Figure 4.

    Figure 4. Android device test configuration.

    Figure 5 provides the double difference GPS L1 C/A pseudorange residuals between two Android devices. We see errors beyond 100 meters and a standard deviation across all data of 14.4 meters. A precision system (RTK or RTX/PPP) would use a standard survey quality base or network of bases and not an Android device for the correction data.

    Figure 5. Short baseline double-difference pseudorange, Android devices.

    Consequently in a typical operating mode where a precision data stream provides corrections, the contribution in a double difference from the pseudorange on the Android devices would be roughly half the Android-to-Android residual seen in this test or approximately 7.2 meters for this example.

    For comparison, the same metric was generated between two precision GNSS units connected to antennas on the same roof. While the data was not from the same time period, we observe very consistent performance over time.

    Figure 6 shows the same pseudorange double difference across a short baseline over 24 hours. When comparing Figures 5 and 6, note the difference in the scale on the pseudorange residual axis. The standard deviation from a pair of precision devices is 53 centimeters (cm) or 27 times lower noise than an example pair of Android devices.

    Figure 6. Short baseline double-difference pseudorange, precision devices.

    All phones that provide GNSS measurements via the Android API publish the phase data in the accumulated delta range field. An accumulated delta range is not necessarily a full phase measurement; it can have an arbitrary starting phase.

    For example, in a precision GNSS receiver, if the receiver locks to a satellite and some time later locks a second channel to the same satellite, the phase measurement from the two channels may have a different integer cycle component, but the subcycle component would be the same except for millimetric tracking noise.

    If the two channels are providing accumulated delta range the initial phase offset may differ by up to one cycle. From the population of Android devices that publish phase that we have tested we have not observed any devices that deliver true full phase.

    They all deliver an accumulated delta range with an arbitrary phase offset. This limits a phase engine to float processing and ambiguity fixing is not possible. The Android phase data collected from the previously described experiment was processed to provide the double difference carrier residuals. This is shown in Figure 7.

    Figure 7. Short baseline double-difference phase residuals, Android devices.

    The y-axis is in millicycles (1,000 millicycles = 1 cycle or approximately 19 cm for L1 GPS). Jumps are seen as the reference satellite changes or when the measurements have cycle slips. In this case the standard deviation is 342 millicycles. A double difference residual on a precision receiver in a similar environment with a high-quality antenna on a short baseline is an order of magnitude lower than this.

    Another useful metric to review are the number of reported cycle slips. Figures 8 and 9 show a comparison of the cycle slips reported on GPS L1 C/A from an Android device compared to data logged on a precision receiver over the same time span. The receiver tends to only cycle slip at low elevation; the device had a zero-degree mask. The Android GNSS device cycle slips at higher elevations, probably a result of deep multipath fades due to the poorer antenna.

    Figure 8. Cycle slips, Android device.

    Figure 9. Cycle slips, precision device.

    In an ION GNSS+ 2017 paper, we showed the achievable position performance using an RTK engine that had been previously customized to operate with measurements from consumer GNSS chipsets. It operated in a float mode due to the sub-cycle issue found in phase data from Android devices.

    We also demonstrated the performance from a precision code-based PVT engine that had changes to the a priori measurement error estimate, a modified pseudorange/Doppler Hatch filter and used SBAS data to correct the position. As very few current Android devices deliver phase information the two engines were used to analyze what is possible today with the pseudorange and may be available in the future as phase is more universally available.

    Data was processed from a Nexus 9 tablet, the only known Android device that has GNSS power duty cycling disabled. The unit was unmodified and so the Android tablet’s integrated GNSS antennas were used. The 2D performance is given in Table 1.

    Table 1. 2D performance from Nexus 9 Android tablet.

    Only GPS L1 and GLONASS L1 measurements were used and the RTK float solution delivered similar performance to the pseudorange solution. This is due to a combination of issues, very high pseudorange noise, and a significant number of cycle slips (see Figures 5 and 8). Only single frequency data was available, and while the engines used had been tuned for consumer data, they were not specifically designed for this class of data.

    Next-Generation Phones

    Within the next couple of years improved chipsets are expected to be available to consumers that will result in improvements in achievable positioning performance. In May 2017, Broadcom provided us with a development kit for its next generation L1/L5 multi-system BCM47755 GNSS chipset. This allowed us to assess what may be possible when improved GNSS chipsets are integrated in the next generation of cellular devices.

    Figure 10. Broadcom BCM47755 development system.

    The development environment included the GNSS chipset with an external antenna port so both a cell-phone equivalent antenna and a precision antenna could be compared. This allowed us to evaluate the impact of the antenna performance on the GNSS observables and positioning results. The Broadcom GNSS development system communicates via USB to a Samsung S7 phone and publishes data via the Android GNSS measurement API so the equivalent data flow of an integrated cellular device is maintained (see Figure 10).

    In our ION paper, we showed the typical phase double-difference residuals observed from current Android devices. The Broadcom BCM47755 originally provided similar performance, although it also supports GPS L5 and Galileo E5A. In November 2017, Broadcom provided a firmware update that resolved the sub-cycle phase issues. With the updated Broadcom software, the double difference carrier residuals for GPS L1 on a zero baseline when differencing a precision receiver to a Broadcom BCM47755 are shown in Figure 11.

    Figure 11. Precision GNSS to Broadcom BCM47755 zero baseline double difference carrier-phase residuals.

    The standard deviation is 45 millicycles which is approximately 8.6 millimeters (mm). This is substantially better than earlier implementations of the Android GNSS interface (see Figure 7) and sufficient to perform RTK ambiguity resolution.

    The rest of the results in this article were obtained with the improved firmware along with a new precision position engine. This engine was designed from inception to support GNSS measurements with differing quality and so can more optimally process the Android GNSS data. The effect of the improvements to the Broadcom firmware and the change in the processing engine can be seen if the results in our ION paper are compared to the data in this section.

    To attempt to model what may be possible with a phone based on a next-generation chipset, a cell-phone equivalent antenna provided by Broadcom was used in some of the tests with the development system, as shown in Figure 12. This device has separate feeds for L1 and L5.

    Figure 12. Cellular equivalent antenna.

    Datasets were collected with the multi-frequency GNSS BCM47755 device. The data was captured in the Android GNSS measurement API format and converted to proprietary format files for further processing. All data was collected in Sunnyvale, California.

    Measurements from GPS L1/L5, Galileo L1/E5A, GLONASS L1 and BeiDou B1 were logged and analyzed. The Precise Positioning Engine (PPE) allows performing carrier-phase RTX and RTK and a pseudorange-based solution using the RTX corrections. Tests were performed by using a precision antenna and a cell-phone equivalent GNSS antenna.

    With Precision GNSS Antenna

    These datasets were collected on a zero baseline with a precision receiver to allow a direct comparison of results with a professional receiver. The first test was on Nov. 22, 2017, where the Broadcom GNSS chip and the receiver were connected to the same professional antenna.

    As seen in Figure 13, both GNSS receivers provide centimeter-level accuracies after some convergence time. With the current satellite constellations, only a third of the GPS satellites have L5 and only about half of the E5-capable Galileo constellation is in space. During this 3.5-hour test, the number of dual-frequency measurements processed by the engine that used the Broadcom chipset — data that does not support L2 — ranged between 6 and 10 satellites (Figure 14).

    Figure 13. RTK performance for a 3.5-hour dataset sampled on Nov 22. Broadcom chip at left and precision chip at right. A short baseline was used — precision antenna.

    Figure 14. Number of GPS L1/L5 plus Galileo E1/E5A dual-frequency measurements used by the position solution based on the Broadcom chipset — precision antenna.

    Convergence times were measured with post-processing tools by splitting the datasets into individual time spans. Figure 15 shows that the consumer GNSS chipset is able to get fixed ambiguity solutions but it takes considerably more time (266 seconds versus 4 seconds) for the 95% of initializations. However, the system is fixing ambiguities and provides centimeter level positioning.

    The same datasets were also processed with RTX-Fast in California. Thus the base station data was replaced by a global/regional correction stream received from an internet-based data source (Figure 16).

    Figure 15. RTK initialization performance, dataset sampled on Nov 22. Broadcom chip at left and precision receiver at right — precision antenna.

    Figure 16. RTX performance for a 3.5 hour dataset sampled on Nov. 22 (Broadcom chip at left and Trimble chip at right) — precision antenna.

    Horizontal accuracy for Broadcom reach 10 cm while the precision receiver reaches better than 3 cm. The degradation is in part due to the difference in quality of the carrier phase and the different number of dual frequency satellites processed. Precision devices provide measurements on E1/L1, L2 and L5/E5 providing at least dual frequency data from GPS, GLONASS, Galileo, BeiDou and QZSS.

    The Broadcom chipset tested provided dual frequency GPS and Galileo along with single-frequency GLONASS and BeiDou; however, due to limited BeiDou constellation visible in California, data from this constellation was not used.

    Convergence was also analyzed and is shown in Figure 17. From the data, we generated 24 convergence runs by taking one hour, progressively shifting the start time by 5 minutes and running the data with different start times through the PPE engine. This produced 24 runs, which were translated into 68% and 95% convergence statics shown.

    Figure 17. RTX convergence performance for a 3.5-hour dataset sampled on Nov. 22. Broadcom chip at left and precision chip at right — precision antenna.

    Figure 18. Code RTX performance for 3.5-hour dataset sampled Nov. 22 and corresponding RTK and RTX phase solutions — precision antenna.

    The RTX-Fast solution for Broadcom reaches 30 cm horizontal error in 68% of the cases in approximately 12 minutes. The RTX-Fast convergence using precision GNSS data is near instantaneous as can be seen in the right of Figures 16 and 17, reaching centimeter accuracy.

    The code position solution using the RTX correction stream provides sub-meter positioning (Figure 18).

    As a summary, the cumulative distribution function plots (Figure 19) show the performance differences for this static environment, on Nov. 22.

    Figure 19. CDF plots for the different PPE position solutions — precision antenna.

    Cell-Phone GNSS Antenna Results

    Similar tests were performed using an external cell-phone GNSS antenna, which is close to the antenna used in a typical smartphone. RTK performance shows centimeter-level accuracies and reasonable convergence times, which are slightly worse than the results with the professional antenna (Figures 20–24).

    Figure 20. RTK positioning and initialization performance for the Broadcom chip and the cell antenna sampled on Nov 20 — cell-phone GNSS antenna.

    Figure 21. RTX-Fast positioning and convergence performance for the Broadcom chip and the cell antenna sampled on Nov. 20 — cell-phone GNSS antenna.

    In general as expected we achieve worse performance when connected to the GNSS cell-phone antenna for all the different positioning modes. For the cell antenna we also generated single-frequency RTK and single-frequency RTX-Fast position solutions and compare it with a code positioning solution.

    Positioning Engine in Android

    Figure 22. Number of GPS L1/L5 plus Galileo E1/E5A dual-frequency measurements used by the position solution based on the Broadcom chipset — cell-phone GNSS antenna.

    The results presented in this article captured GNSS data using the Android API and then post-processed the data using PC versions of the position engines. A significant amount of data has been captured and analyzed using this method.

    For the purpose of real-world demonstration the PPE has been implemented in an Android app to be used in cell phone devices. This PPE is able to provide RTK, RTX and code based positioning technology in one single PPE library.

    The app has been tested running on a Samsung S7 connected to Broadcom’s new chipset development kit as well as a Nexus 9 tablet that uses an older generation GNSS chipset.

    Figure 23. Code RTX performance, the dataset sampled Nov. 20 and corresponding RTK and RTX phase solutions — cell-phone GNSS antenna.

    Future work will refine this solution as well as evaluate how well the system works when mobile. The data collected in this article operated in an environment with a clear view of the sky. We plan to characterize what happens when the platform moves with both pedestrian and automotive dynamics, as well as the effects of body masking and challenges with changes to the GNSS antenna reception pattern when the phone is held.

    Summary

    While this article has highlighted that sub-meter and centimeter accuracy have been achieved in a laboratory environment, there are many challenges to be addressed before centimeter accuracy in a phone can be achieved with performance suitable for users in real-world environments.

    Figure 24. CDF plots for the different PPE position solutions for cell antenna dataset.

    The challenges include very high multipath, significant differences in the tracking performance between different devices, and high rates of cycle slips. As very few Android-based devices provide continuous phase, a pseudorange/Doppler-based engine has been modified to accept Android data.

    Based on the testing with existing devices it is possible to achieve position solutions of 1–2-meter accuracy in ideal static scenarios. This is a significant improvement in accuracy for Android based devices.

    Figure 25. PPE engine on a Samsung S7 with a Broadcom BCM4775 evaluation kit.

    However, as performance differences were observed between different mobile devices significantly more data needs to be collected over a larger set of devices to review the repeatability of these preliminary results from existing Android devices.

    The Broadcom BCM47755 development kit for a dual-frequency GNSS chipset intended for future phones has allowed us to review the potential position performance that may be achievable in a handset in a few years.

    By connecting this next-generation GNSS chipset to a GNSS antenna typical of a cellular device and comparing the performance from a precision GNSS antenna, we’ve shown for the first time that it is possible to produce precision positions from a static cellular class GNSS device in ideal conditions at the centimeter level with both an RTK solution and a PPP solution.

    However, due to the significantly higher measurement noise and high multipath from the cellular device’s GNSS antenna, the convergence times to reach centimeter level remain a challenge; although using dual-frequency phase data from a cellular GNSS chipset with a PPE and RTX service, the position is very rapidly sub-meter.

    Future work will focus on analyzing how the performance changes when operating in the normal user environment. The effects on the measurements of user motion, body masking and de-tuning of the antenna when the device is held need to be quantified. The Nexus 9 tablet used in this article does not have integrated cellular. The Broadcom development kit connects to the phone via a cable and is also not integrated into the handset.

    We will be evaluating what may happen with a more integrated unit to make sure emissions from devices with integrated cellular very close to the GNSS antenna do not result in further degradation.

    As the position performance is very sensitive to the quality of the antenna from both multipath and cycle slips due to low C/NO and deep fades, we’ll also evaluate how well the performance of the PCB-based GNSS antenna, which is part of the BCM47755 evaluation kit, matches current handsets.

    Acknowledgment

    This article further develops work first shown in an ION GNSS+ 2017 paper, “On the Path to Precision — Observations with Android GNSS Observables.”

    Manufacturers

    Trimble CenterPoint RTX is the satellite orbit and clock corrections service used here, enabling a PPP-like positioning with ambiguity fixing, providing better than 4 cm with typically less than 10 minutes’ convergence.

    RTX-Fast functionality in Europe and parts of California uses regional atmospheric models to provide better than 4-cm horizontal in typically less than one minute. When precision and professional receivers and RTK engines are mentioned in this article, they are Trimble devices, the BD940 receiver in some cases.

    A Trimble Zephyr 3 antenna was used in many tests shown here.

  • Altair markets narrowband cellular chipset with integrated GNSS

    Altair markets narrowband cellular chipset with integrated GNSS

    LTE chipset maker Altair Semiconductor has demonstrated GNSS functionality integrated in its new ALT1250 narrowband CAT-M1 and NB1 (NB-IoT) chipset.

    In addition to GNSS functionality, the ALT1250’s extreme level of integration eliminates the need for most external components required to design a cellular Internet of Things (IoT) module.

    Its GNSS capability was demonstrated June 13  at the Sierra Wireless Innovation Summit being held at the Paris Novotel Tour Eiffel in Paris, France.

    Approximately the size of a shirt button and less than 100 mm2 in size, an ALT1250 module features support for both Release 13 standards — CAT-M1 and NB1, and includes a wideband RF front-end supporting unlimited combinations of LTE bands within a single hardware design, a multi-layered and hardware-based security framework, an internal application MCU subsystem and packaging that enables standard, low-cost PCB manufacturing.

    “Location determination is essential in many IoT applications — including asset tracking, vehicle monitoring and wearable devices. Satellite positioning is the most accurate method for doing that,” said Eran Eshed, co-founder and vice president of marketing for Altair. “Integrating GNSS functionality in the ALT1250 significantly reduces the overall cost of IoT solutions while offering state-of-the-art, low-power satellite positioning capabilities in a miniature package. The market is responding well to it.

    “We called the ALT1250 a game-changer when we announced it several months ago — integrated GNSS is one of a large set of groundbreaking innovations offered by this chip.”

     

  • The economic benefits of GPS

    The economic benefits of GPS

    Table 1. Preliminary 2013 U.S. GPS economic benefit estimates. (Chart: GPS World, based on data from author)
    Table 1. Preliminary 2013 U.S. GPS economic benefit estimates. (Chart: GPS World, based on data from author)

    This article is based on a presentation to the National Space-Based Positioning, Navigation and Timing Advisory Board in June 2015. The study reported on at the meeting was requested by the National Executive Committee for Space-Based Positioning, Navigation and Timing. It demonstrates the widespread use and importance of GPS to the U.S., with estimated benefits in 2013 of about $56 billion, or 0.3% of GDP for a subset of applications. The study is the first part of an effort that is expected to refine and extend this analysis.

    By Irv Leveson

    Critical to many civilian applications and innovations, GPS brings great economic benefits. These benefits have grown rapidly with the integration of GPS with other technologies and its wider and deeper infusion into applications. New GPS signals and other improvements in the system will further expand and enhance use. The unmistakable conclusion: GPS is everywhere.

    Benefits of GPS to the U.S. will increase with the availability of other GNSS systems, even though GPS will constitute a smaller share of global GNSS benefits. The U.S. will continue to provide leadership, standards and innovation in technology and applications with positive domestic feedback.

    GPS and other GNSS and enhancements raise productivity; reduce and avoid costs; save time; enable improved and new production processes, products and markets; increase health and well-being; reduce injury and loss of life; improve the environment; and increase security.

    The National Executive Committee for Space-Based Positioning, Navigation and Timing (PNT), which is responsible for maintaining U.S. leadership in GNSS, commissioned a study to assign a quantitative value to the broad economic uses of GPS. The purpose is to inform the public, federal decision makers and critical infrastructure owners/operators on the importance of GPS and the need to protect it from disruption. Assessing the economic implications of actions such as preventing or disallowing interference, spectrum reallocation, developing supplementary or backup systems and/or toughening receivers can be informed by value estimates and the data used to derive them. In addition, economic values can contribute to planning for GPS modernization and analysis of budgets. Baseline estimates facilitate comparisons with future developments. GPS benefit estimates will be “ballpark” no matter how sophisticated the methodology because of limits to the availability of information, but in many cases, knowing orders of magnitude is essential in choosing courses of action.

    Widespread, Pervasive Impact. The technological environment is one of rapid changes in information and materials technology and integration of technologies at levels ranging from systems on a chip to large-scale systems. GPS is increasingly integrated with other technologies and systems that build on each other to achieve greater outcomes.

    The U.S. Department of Homeland Security counts GPS as an enabling technology because of its crucial role in 14 of the 16 industries that are classified as part of the nation’s critical infrastructure. It is useful to view GPS’ role as being especially important in “enabling the enablers,” industries that particularly support the rest of the economy and are at the forefront of economic growth. The most notable of these are transportation, communications, power and financial services.

    Economic Value versus Impact

    Economic value is the addition to the value of the economy from the provision of a good or service, or the introduction of a technology. Benefits are measured relative to what would have been expected if there were no GPS. Direct economic value is the increase in value in using sectors. Total economic value includes increases in value to suppliers and value induced in the rest of the economy.

    Direct economic impact, on the other hand, refers to measures of the importance of sectors that are using GPS. Total economic impact is the importance of sectors affected by GPS, whether they are using it or not. Total economic impact of GPS is virtually the size of the whole economy, so it is not very meaningful.

    Direct economic impact is measured by value added of using sectors when the purpose is to avoid duplication among sectors that buy from and sell to each other. It may be measured by revenue for a single sector when adding sectors is not involved, so there is no need to avoid duplication.

    The distinction between economic value and economic impact is critical. Even if economic impact is measured by value added rather than revenue, the value is not the net addition to the economy from the use of the product or technology. It is only the size of the using sector. See Figure 1.

    Figure 1. Measuring GPS economic value and economic impact. (Chart: author)
    Figure 1. Measuring GPS economic value and economic impact. (Chart: author)

    The GSA Study

    The most comprehensive estimates of global GNSS market size come from the European GNSS Agency (GSA), which has released four market reports from 2010 through 2015. The data are measures of economic impact and not economic value. The reports are of great interest because of their comprehensive global look at the sizes of markets and inclusion of forecasts. In contrast, the emphasis in this part of the present study is on current economic value, with U.S. benefits assessed for GPS.

    One reason for interest in the GSA reports is that market information and projections often are proprietary and there can be great inconsistency across market research studies. GSA makes use of many confidential studies without revealing which sources contributed to each estimate. It apparently has been allowed to incorporate proprietary information from a number of market research firms since the data is subsumed in GSA’s own estimates and/or presented in graphs for which underlying numbers are not provided — and from which it is often difficult to even roughly extract them.

    The 2015 report stated the methodology as: “The underlying forecasting model uses advanced forecasting techniques applied to a wide range of input data, assumptions and scenarios…Where possible, historical values are anchored to actual data.” Results were checked against opinions of market segment experts and market research reports. However, these analyses are not provided in the reports and have not been made available.

    A distinction is made between the core market which covers the value of components that provide GNSS functionality in devices and enabled markets which “represent the services and devices enabled by GNSS.” The 2015 report provides global data on both core and enabled market and goes into much more detail on core markets for application sectors. In addition to providing sector information that did not appear previously, the 2015 report presents data on the extent to which each combination of the GNSS constellations was supported by receivers or chipsets offered by suppliers. Additional information on enabled sectors is in earlier reports.

    GSA found in its 2015 market report that:

    • 3.6 billion GNSS devices were in use globally in 2014, of which 3.08 billion were smartphones and .26 billion were for road.
    • North America had about 450 million devices installed (about 80% U.S.).
    • North America had 1.4 devices per capita in 2014.
    • North American shipments were 250–300 million in 2013.

    Global core revenue was estimated at roughly €62 billion and enabled revenue at €227 billion in 2014. As noted, core revenue includes GNSS device components, software and services, while enabled revenue refers to applications.

    Location-based services (LBS) was projected to account for 53.2% of 2013–2023 core revenue growth, and road for 38%.

    North American-based companies had sizeable shares of the global GNSS core market in 2012, particularly among component manufacturers. (See Table 2). Their market share among system integrators was highest in aviation.

    North American-based companies had a 44% market share of value-added services revenue in 2012.

    Table 2. North America-based company shares of Global GNSS core market, 2012. (Chart: author)
    Table 2. North America-based company shares of Global GNSS core market, 2012. (Chart: author)

    Markets and Applications

    The pervasiveness of GPS-enabled applications is illustrated by the following statistics:

    • 900 million mobile phones that incorporated GPS were sold globally in 2012.
    • The U.S. had 188 million smartphone subscribers and 263 million Internet users in 2013.
    • 20% of U.S. mobile phone users get up-to-the-minute traffic or transit information.
    • The new industry category in the 2012 North American Industrial Classification System: “Internet publishing and broadcasting and web search portals” had U.S. revenue of $87 billion and 181,000 employees in 2012.
    • Google estimated that its search and advertising tools provided $111 billion in economic activity in the U.S. in 2013.
    • Deloitte estimated that Facebook enabled $104 billion of economic impact and 1.2 million jobs in North America in 2014.
    • Google Play and the Apple App Store each had more than 1.2 million apps in 2014.

    How GPS Is Used. Uses of GPS include:

    • In agriculture for auto-steering tractors, combines and sprayers for precise operation, variable rate technology for precise placement of seed, fertilizer and pesticides, and for yield monitoring.
    • Managing forest health and ecological restoration, reducing fire and other hazards, and harvesting forest products.
    • In commercial fishing, navigation, finding fishing locations and monitoring fish catch by authorities.
    • In construction to direct the movement of dozers, excavators, pavers, scrapers, compactors and other heavy equipment and the placement of blades to give precise results.
    • In open-pit mining to guide loaders, dozers, drills and draglines.
    • In offshore energy exploration and development, for drilling, installations, pipe laying, diving operations, pipe inspection, repair and abandonment.
    • In surveying, to greatly reduce costs and to improve quality of products that rely on it.
    • In aviation, for navigation and monitoring positions of aircraft and for satellite-based augmentation systems (WAAS in the U.S.). GPS is the principal source for navigation for aircraft equipped with Area Navigation (RNAV) or Required Navigation Performance (RNP).
    • Railroad train pacing systems for cruise control, positive train control to keep track of train location and movement authorities, track defect location, and locating trucks with rail workers.
    • In marine transportation, for navigation, collision avoidance, communications and situational awareness and for monitoring by offshore authorities.
    • In vehicles, with handheld and embedded devices for navigation and fleet management.
    • For precise timing and time synchronization and frequency coordination (syntonization). It is used most notably in broadcasting and communications, including both cell phones and traditional telephone applications and the Internet, so packets arrive at the same time, for power generation and distribution to locate problems, and in financial services for time-stamping transactions.
    • In first responder services for location, navigation and communications and in emergency warnings and evacuations.
    • In structural monitoring of dams and bridges.
    • In environmental monitoring, including vegetation growth and sea-level change.

    LBS and GIS

    Rapid growth is taking place in location-based services (LBS) and geographic information services (GIS), which include everything from indoor location to many aspects of the Internet of Things and the “sharing economy,” and sophisticated systems for information management, analysis and display.

    GPS is used for tracking and inventorying assets ranging from heavy machinery on farms and construction and mining sites, to pipes and other materials, containers in trucking sites and ports, and the location of utilities in the ground. In logistics it facilitates planning of product flow and transport.

    The growth of same-day delivery — which takes advantage of Internet, cell phone, and location and navigation technologies enabled by GPS — is a continuation of the growth in just-in-time delivery that has been a phenomenon in manufacturing for several decades. Now it is having a profound effect on wholesale trade, retail trade and transportation.

    The size of the LBS and GIS sectors is not defined and measured in a consistent way, and except for vehicle use, there is little information on productivity and saving in costs and time. (See sidebar box.)


    LBS and GIS Market Size Estimates

    For LBS and GIS, definitions and measures can vary greatly and often are not explicit.

    Location-Based Services Market Size Estimates

    • Frost & Sullivan estimated the global LBS market at €22.8 billion in 2012 and forecast €32.0 billion in 2015.
    • Market and Markets estimated global LBS revenue at $8.1 billion in 2014.
    • Berg Insight estimated North American LBS revenue at $835 million in 2012.

    (The U.S. can be assumed to spend 20–25% of the world value and about 80% of the North American value.)

    Geographic information Systems Market Size Estimates

    • BCG estimated revenue of the U.S. GIS industry at $73 billion in 2011.
    • The global GIS market will reach $10.6 billion in 2015, according to a report of Global Industry Analysts in 2013.
    • The Canadian Geomatics study found private-sector spending of $2.3 billion in 2013. If U.S private spending was the same percentage of GDP, it would be $23.6 billion.

    International Trade

    Official data show a $2.3 billion U.S. deficit in trade in GPS equipment in 2013. This gives an incomplete and misleading picture of the role of the U.S. and the benefits that result. See Figure 2.

    Figure 2. U.S. trade in GPS equipment, 2013 (millions of dollars). (Chart: author)
    Figure 2. U.S. trade in GPS equipment, 2013 (millions of dollars). (Chart: author)

    The trade numbers for GPS equipment do not include revenue for licensing, international payments received by social media and e-commerce companies, or other Internet-based revenue for which the U.S. may have a substantial net trade surplus and which are an important source of revenue and profits of U.S.-based companies.

    Imports of GPS equipment software and services enable the U.S. to gain more efficient production in many applications at home and enable the U.S. to export more goods and service that rely on GPS.

    Exports of GPS equipment come back to the U.S. as components that benefit U.S. businesses and consumers with more capable products and lower prices. Exports of GPS equipment enable other countries to build on the technologies and contribute to innovation, while imports enable the U.S. to share in foreign innovations. Exports of GPS equipment and associated knowledge also raise incomes in other countries, creating larger markets for U.S. goods and services.

    Scope of Benefit Estimates

    The U.S. benefit estimates reported here are the result of an initial effort and are not meant to be comprehensive. More work is expected to be done to fill in some of the gaps.

    Sectors were chosen based on availability of information to permit relatively robust estimates and importance to the economy or policy issues. These considerations limited the number of sectors for which estimates could be made. Methods were determined based on the nature of available studies and varied among sectors. Only economic benefits were included, with health and safety and environmental benefits left for later research.

    Benefits include the value to users above their costs (consumer surplus). Benefits of GPS are compared with alternatives without GPS or an application using it (counterfactuals). Estimates are gross. They are not reduced by the costs of achieving the benefits. Contributions of augmentations are included, since a quantitative basis for separating them is not available.

    Estimates were primarily benefits through productivity and cost savings in operations, with savings in input costs included where their magnitudes were clear. Benefits to the rest of the economy are not included. Illustrative allowances were made for the contributions of other technologies and systems to the outcomes examined.

    In the case of GPS timing, the estimates were based on the costs avoided by not having to develop an alternative timing source on the assumption that the type of alternative source possible would have evolved from the time GPS became available. The measure does not represent the value of GPS time and synchronization to the nation and to users relative to the absence of a precise time and frequency source.

    Government was included in the estimates for construction, surveying, and fleet and non-fleet vehicles. For timing and non-fleet vehicle benefits, two alternative measures are averaged. Sectors with lower quality estimates ­— rail and maritime transportation — were included because of their importance to the economy. Shares of benefits attributable to GPS were rough assumptions. More robust estimates would require extensive data collection and interviewing in studies greatly exceeding available time and resources.

    The primary focus was on productivity improvements, cost savings and cost avoidance, where costs include users’ time. Productivity increases and cost reductions allow more to be produced with the same amount of resources in the sectors utilizing the technology or allow resources to be freed up for other purposes. In that sense, they are equivalent.

    When benefits are measured by productivity gains or cost savings, much of consumer surplus (the value to users above what they pay) is implicitly included. Some sources measure value by willingness-to-pay. Willingness-to-pay includes consumer surplus. It also encompasses costs of the purchase and other costs incurred by the user.

    Criteria for Selecting Sectors

    The potential for making sector estimates of economic benefits was categorized in three basic levels:

    confident: based on robust estimates.

    indicative: based on one or more less robust estimates.

    notional: illustrative, if major contributions of other technologies are not separated and estimates must be based on a plausible percentage of a larger benefit, or if information is not available and estimates must be based on a percentage of market size.

    Choices among categories for estimation and estimation methods depended not only on which of the basic criteria are satisfied but also on the following additional criteria:

    • The importance of the sector to the economy, for example as an enabler of other activities.
    • The potential use of benefit estimates for the category as an input into analyses of the effects of signal disruption.

    Several dozen studies were assessed to determine categories for inclusion and to select studies that can form the basis of estimation. Studies for use in estimation of benefits in a category were chosen according to how well they met the following criteria:

    GPS. A test of introduction of GPS or comparison with and without GPS rather than benefits of a broader service.

    Coverage. Estimates that cover a major part of the category.

    Robustness of estimates, including the type of review the source is likely to have had.

    Consistency. If alternative better estimates are not in such a wide range that an average is less meaningful except where explainable by expected sources of variation.

    Timeliness. Preference to a recent period being covered by the estimates.

    U.S. Economic Benefit Estimates

    Preliminary estimates of economic benefits for included U.S. sectors totaled $55.8 billion in 2013. Averaging the alternative estimates, the sum of the benefits in the two vehicle categories is $25 billion, by far the largest of the sectors estimated. Next were agriculture with $13.7 billion, and surveying with $11.6 billion.

    Economic benefits are underestimated for several reasons. Some sectors are not included because of lack of information on productivity and cost savings, namely LBS other than vehicle, including asset tracking and locating people; GIS and mapping other than nautical charts, forestry, fisheries, mining, energy exploration and development, land and coastal management, weather, and scientific applications and space.

    Parts of others are not included: non-grain agriculture, construction other than earthmoving, GPS in aviation for some Area Navigation (RNAV) Standard Instrument Departure Routes (SIDs) and Standard Arrival Routes STARS) and Required Navigation Performance (RNP), and rail other than positive train control.

    Some estimates are conservative. The value of saved time in non-fleet vehicle transportation is based on the recommendation of the Transportation Research Board rather than the much higher value used by the U.S. Department of Transportation.

    Some types of benefits are not included — specifically, benefits of GPS timing applications above the cost of alternatives, and avoided income loss, property damage and medical costs associated with reduced accidents and improved emergency response.

    Increases in benefits between 2003 and 2005 are not estimated.

    And, as indicated, non-economic benefits such as those to health, safety, security, reduced loss of life and to the environment are not yet addressed.

    Benefits as measured thus far are about 0.3% of GDP in one year. If all of the excluded sources of benefits were quantified, the benefits would be much larger.

    Estimating Benefits for Sectors

    U.S. economic benefits of GPS for grain farming were estimated for farms with grain sales of $250 million or more. The same method as was applied for earthmoving in construction.

    A composite range of percentages of productivity gains and cost savings of 18–25% was determined from various studies. In the case of grain farming, benefits also come from yield increases due to improvements in plant health. The productivity gains used in the calculations incorporated both sources of benefits. Productivity was taken together with market size and an estimate of 68% adoption of technologies taking advantage of GPS to compute initial estimates of benefits. A notional adjustment was then made to exclude the contributions of other technologies and GNSSs. While having the adjustment determined by a group of experts would have been preferred, that was not possible with the time and resource constraints of the study.

    Benefits of GPS machine guidance with earthmoving in construction were calculated based on an 8–12% share of construction for earthmoving operations, a benefit of 18–22% and a 20–25% adoption rate, relying on a number of sources.

    For surveying, an estimate of market size was constructed based on U.S. Bureau of Labor Statistics data on numbers of surveyors, cartographers and photogrammetrists in the engineering services industry vs. the rest of the economy, together with revenue data for private surveying and mapping from the Economic Census. This was combined with a composite estimate of productivity gains over conventional surveying of 45–55% and an assumption of 100% adoption.

    The benefit values for air transportation were estimated for the study by the Federal Aviation Administration (FAA) based on effects of WAAS and performance-based navigation (PBN). The rail estimates cover only positive train control, which is in early stages of implementation. Information is highly uncertain, but impacts as of 2013 are small. Maritime benefits were based on updating an earlier estimate of benefits of the private-sector value of nautical charts. The estimates for fleet vehicle-connected telematics were based on savings found in an extensive survey of fleet customers over a five-year period.

    Timing benefits were based on the avoided costs from not having to develop an alternative source of timing. Alternatives considered were eLoran and a system of three geostationary satellites. Since there would have been strong pressures to develop an authoritative timing source in the absence of GPS timing, it was assumed that one of the alternatives would have been developed rather than assuming as in other cases that technologies in use when GPS became available would have continued in use.

    Two estimates also were made for consumer and other non-fleet vehicle use. One was based on extrapolating results of a study of consumer willingness to pay for navigation services, and the other on time saved by navigation services.

    Part of the benefits of LBS other than those that are vehicle-related and for GIS are implicitly included in estimates for sectors that use them.

    Data and Research Needs

    Additional work would be desirable to extend and refine the GPS economic benefit estimates, quantify safety-of-life and environmental benefits, examine international benefits, assess potential future benefits and consider loss from denial of GPS. Benefits of many new and rapidly growing services are yet to be quantified.

    Systematic research is needed to fill in gaps in adoption, productivity and cost savings with comparative before-and-after studies as well as with case studies. Robust studies require major and often multi-year efforts involving targeted data collection, which are rarely done by government or academics for GNSS. Information needs to be much more granular, taking into account specific functions in which GNSS is used (such as plowing, seeding, fertilizing, harvesting), specific GNSS and non-GNSS technologies employed in each function at each site, and extent of their use.

    Also, results for GPS might be improved or at least be more acceptable if the contribution of other technologies and GNSSs to measured benefits were assessed by a group of knowledgeable individuals rather than by a single researcher.

    Information on market size, penetration and growth from market research firms, which tends to capture recent developments, is based on greatly varying sources and methods, resulting in major gaps and great divergence in estimates, especially in new or rapidly growing areas like LBS and GIS. The North American Industrial Classification System (NAICS) and its application in federal data collection such as in the Economic Census lags far behind in recognizing new categories and providing sufficient detail. Lags in data collection and research lead to understatement of the use and benefits of GPS.

    Looking to the Future

    Future benefits are expected to be even greater because of evolution of technologies, expansion of GNSS systems, creation of new products and markets, and growth and penetration of markets. The possibilities are suggested by the numerous nascent applications that have been emerging. Many will be enabled by expanding GNSS systems, signals and capabilities in conjunction with geographic expansion and increased capabilities in wireless systems.

    The progression of platforms is long and growing: mainframes, PCs, mobile phones and other handheld devices, tablets, game controllers, wearables, TVs, home appliances, air and space — including planes, UAVs, satellites, planets, moons, rovers, rockets and spaceships.

    The widespread availability of platforms and the growing ability to utilize them promises a long way to go in developing applications and deriving benefits.

    Acknowledgments

    The author thanks the PNT Advisory Board and Gov. Jim Geringer, liaison from the board to the study; Jason Kim of the Department of Commerce who oversaw the project; Jim Miller of NASA; and the members of the interagency Economic Study Team that advised the effort. Numerous additional people in and out of government provided information and assistance. Responsibility for the content and findings rests with the author.


    IRV LEVESON, who has a Ph.D. in economics from Columbia University, is an economic and strategy consultant and founder of Leveson Consulting. He has done extensive work on GNSS markets and issues for more than 10 years. He is a member of the Institute of Navigation, the American Economic Association and the National Association for Business Economics.

  • GSA’s 2015 Report Dives Deep into Global GNSS Market

    GSA’s 2015 Report Dives Deep into Global GNSS Market

    FIGURE 1. Cumulative core revenue, 2013–2023.
    FIGURE 1. Cumulative core revenue, 2013–2023.

    2015 GNSS Market Report: European GNSS Agency Provides a Fresh Look at Worldwide Growth

    The fourth edition of the European GNSS Agency’s (GSA’s) GNSS Market Report provides a comprehensive source of knowledge on this dynamic global market. The report has become a key reference for organizations building their GNSS market strategies. The new edition provides:

    • Comprehensive updates on previous analyses;
    • New statistics of the GNSS receiver capabilities of the 31 top global manufacturers, offering in total more than 300 models;
    • Insights on the GNSS industry and regional shares of the GNSS market
    • A more granular segmentation of the global GNSS market, namely: European Union (EU28); North America (including the United States, Canada, Mexico); Asia-Pacific (including China, Japan, Australia, India, Republic of Korea); Non-EU28 Europe (Norway, Switzerland, Russia, Ukraine);  Middle East and Africa (Turkey, Israel, South Africa, UAE, Saudi Arabia); South America and Caribbean (including Brazil, Argentina, Colombia, Guatemala)
    • Information on a new market segment: Timing and Synchronization
    • Plus additional applications within existing segments, such as recreational navigation, fishing vessels, personal locator beacons, emergency locator transmitters and digital tachograph.

    TABLE 1. Top 10 companies in each group based on 2012 revenue.
    TABLE 1. Top 10 companies in each group based on 2012 revenue.

    Key Findings

    Top-line insights from the fourth GSA GNSS Market Report:

    • The global GNSS downstream market is forecast to increase by 8.3 percent annually from 2013– 2019, then slow down to 4.6 annually around 2023, growing on average faster (7 percent) than the forecast global GDP in this period (6.6 percent).
    • The installed base in the mature regions of EU28 and North America will grow steadily (8 percent per year) to 2023. The primary region of growth will be Asia-Pacific, which is forecast to grow 11 percent per year from 1.7 billion in 2014 to 4.2 billion devices in 2023 — more than the EU and North America together. The Middle East and Africa will grow at the fastest rate (19 percent per year), but starting from a lower base.
    • Location-Based Services (LBS) and Road dominate cumulative GNSS revenues, driven by booming sales of smartphones and in-vehicle devices, location-aware applications and data services.
    • With emerging economies catching up in terms of GNSS devices per capita, the Digital Divide will narrow, driven by the take-up of smartphones. The growing dominance of smartphones (3.08 billion in 2014) is foreseen as the most popular platform to access LBS.
    • In the analysis of the capabilities of GNSS receivers and chipsets, it is reported that more than 60 percent of currently available receivers and chipsets support a minimum of two constellations with more than 20 percent supporting all four of them.

    FIGURE 2. SUPPORTED CONSTELLATION BY RECEIVERS Chart shows the percentage of available receivers capable of tracking signals from one GNSS (such as GPS only), two GNSS (GPS + Galileo, GPS + GLONASS, GPS + BeiDou), three GNSS (GPS + Galileo + GLONASS, GPS + Galileo + BeiDou, GPS + GLONASS + BeiDou) or tracking signals from all constellations at the same time. The percentages add up to 100 percent. We can conclude that almost 60 percent of all available receivers, chipsets and modules are supporting a minimum of two constellations, showing that multi-constellation is becoming a standard feature across all market segments.
    FIGURE 2. SUPPORTED CONSTELLATION BY RECEIVERS Chart shows the percentage of available
    receivers capable of tracking signals from one GNSS (such as GPS only), two GNSS (GPS
    + Galileo, GPS + GLONASS, GPS + BeiDou), three GNSS (GPS + Galileo + GLONASS, GPS +
    Galileo + BeiDou, GPS + GLONASS + BeiDou) or tracking signals from all constellations at
    the same time. The percentages add up to 100 percent. We can conclude that almost 60
    percent of all available receivers, chipsets and modules are supporting a minimum of two
    constellations, showing that multi-constellation is becoming a standard feature across all
    market segments.

    New Charts

    The report includes new infographics presenting:

    • Global GNSS downstream market size, core and enabled (2013 to 2023)
    • GNSS industry share by region (2012)
    • The global shares of companies among components manufacturers, systems integrators and value-added service providers (2012)
    • Capability of GNSS receivers and chipsets, all segments (2015)
    • Supported constellation by receivers and chipsets , all segments (2015)
    • Detailed analysis of key GNSS segments: LBS, Road, Aviation, Rail, Maritime, Agriculture, Surveying, Timing and Synchronization, quantified in terms of:
      • Shipments of GNSS devices by application and region (2013 to 2023)
      • Installed base of GNSS devices by application and region (2013 to 2023)
      • Core revenues from GNSS device sales by application and region (2013 to 2023)
      • Capability of GNSS receivers and chipsets (2015)
      • Supported constellation by receivers and chipsets (2015).

    FIGURE 3. LOCATION-BASED SERVICES SECTOR GNSS shipments by type; GNSS penetration in mobile phones is defined as the proportion of mobile telephones in use in the world that is GNSS enabled.
    FIGURE 3. LOCATION-BASED SERVICES SECTOR GNSS shipments by type; GNSS penetration in
    mobile phones is defined as the proportion of mobile telephones in use in the world that is
    GNSS enabled.

    FIGURE 4. ROAD SECTOR Core revenue from GNSS device sales and services by application.
    FIGURE 4. ROAD SECTOR Core revenue from GNSS device sales and services by application.

    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 provided by expert opinions and model results are cross-checked against the most recent market research reports from independent sources, before being validated through an iterative consultation process with sector experts and stakeholders.

    Download

    Readers can download the entire 29-MB report free.

  • GSA Releases 2012 SatNav Market Report

    The European GNSS Agency (GSA) has published its second Global Satellite Navigation System (GNSS) Market Report, providing key information to entrepreneurs in the satellite navigation sector.

    GNSS market forecasting is of great interest to private and public GNSS stakeholders, for business and strategic planning and policymaking, according to the GSA. According to the 2012 GSA Market Monitoring Report, the worldwide GNSS market is growing fast and the total market size is expected to increase at an average of 13 percent per year until 2016.

    The total enabled GNSS market size is expected to stabilise in the latter half of the decade due to market saturation, price erosion and platform convergence. Global shipments of GNSS devices are lower than previously forecasted up until 2015 yet are forecasted to continue growing to over 1.1 billion units per year.

    Expanding coverage. Following up on the first GNSS Market Report published in 2010, the GSA’s 2012 Report includes an analysis of two new sectors: maritime and surveying. Relevant examples from EU research projects have also been included for each sector.

    2012 Report Highlights

    Road and location-based services (LBS) still in the lead. Road and LBS dominate GNSS device sales (54% and 44% respectively). LBS constitutes 87% of the total GNSS market in terms of units sold and GNSS penetration in smartphones is set to increase from 30% today to almost 100% in 2020. For road navigation, traditional Personal Navigation Devices (PNDs) will gradually disappear from the European market yet remain present in other regions in the form of low cost OEM products. Smartphones and in-vehicle devices will be the preferred means of navigation.

    Commercial aviation use will grow. In the Aviation sector, the segment that will see the greatest growth in terms of GNSS equipment revenues will be Commercial Aviation, surpassing general and business aviation by 2018.

    GNSS use in agriculture continues to rise. In agriculture the current positive growth trend will continue; low cost precision agriculture solutions based on EGNOS are driving GNSS adoption by farmers in Europe.

    Surveying: a growing opportunity. In surveying, the construction segment is dominating the market in terms of units and value. North America is leading in terms of market penetration but the other regions will catch up by 2020 as GNSS is rapidly replacing the traditional surveying and mapping methods in Europe and around the world.

    Safer seas with GNSS. In the open sea segment, shipments of search-and-rescue (SAR) beacons will exceed those of other categories making the SAR segment the largest in terms of shipments and second largest in terms of market size.

    The 2012 GSA Market Monitoring Report can be downloaded for free.