How will widespread deployment of 5G most benefit GNSS?
Greg Turetsky
“The connectivity options that widespread 5G offer will accelerate multiple GNSS benefits. The high bandwidth is starting to encourage many into the RTK domain, but I think the bigger opportunity may come from the low power versions that enable IoT applications. The combination of the ubiquity of cellular connectivity with the low power of NB-IoT could truly accelerate the real time asset management sector all the way down to the package/pallet level.” — Greg Turetzky
Allison Brown
“Widespread deployment and adoption of 5G is likely to continue to increase the demand for spectrum as broadband access continues to expand. The recent FCC decision allowing Ligado to operate terrestrial networks in bands near GPS is likely not the last decision that will result from this increasing demand. It is not clear to me that 5G deployment will ‘benefit’ GNSS and chipset vendors may need to prioritize developing products that have improved robustness in the presence of nearby interference.” — Alison Brown
Miguel Amor
“The benefit of 5G will be seen in the long term, when 5G ranging capability is available. Hybrid positioning algorithms using both 5G and GNSS observations will provide significant positioning benefits in challenging urban environments and seamless navigation between indoor and outdoor environments. Applications across markets will see the benefits of hybrid 5G and GNSS navigation, but the real advantage lies in how this hybrid will enable the future of autonomous mobility. We will see both technologies working closer together to deliver a seamless and ubiquitous positioning solution.” — Miguel Amor
Mitch Narins
“Like communications, the ability to precisely and securely position and navigate is an essential part of 21st century life. Together they must support both critical and non-critical operations. This requires finding a common understanding of spectrum needs and how to have the best of both. In the long run, end runs by either side may achieve myopic goals but will damage society. The problem is crying out for an enterprise-level systems engineering leadership that can plot our future spectrum course. Else, the push for spectrum will continue, fueled by ‘entrepreneurial spirit’ and often a lack of understanding of the importance of other spectrum uses.” — Mitch Narins
Accurate and reliable positioning, timing and navigation (PNT) technologies, such as GPS, have become “invisible utilities” that enable many critical applications, including the electric grid, telecommunications, agriculture and port operations. These systems, however, are vulnerable to accident and attack, including cyber threats and jamming.
Therefore, the Science and Technology Directorate of the U.S. Department of Homeland Security and the National Risk Management Center of the Cybersecurity and Infrastructure Security Agency have been working in collaboration with industry and government stakeholders to develop the Resilient PNT Conformance Framework, which provides a common framework for defining resilient PNT systems and addresses strategic risks to U.S. national critical infrastructure. This work is now transitioning to the Institute of Electrical and Electronics Engineers (IEEE) as the Standards Working Group for Resilient PNT User Equipment (P1952) and will help serve as starting resources for the refinement and development of a standard.
By creating common definitions for different levels of resilient PNT systems, this new standard will enable vendors to differentiate their products from non-resilient PNT systems, as well as enable end-users to make deliberate, risk-informed decisions as to which systems are most appropriate for their applications and needs. The development of this voluntary standard will help influence the future design, acquisition and deployment of resilient PNT systems within our national critical infrastructure.
The IEEE standards process is an inclusive one, designed to gather many stakeholders interested in resilient PNT. If you would like to participate in the standards working group, just notify the group’s chair (Shelby Savage at [email protected]) or its secretary (Patricia Larkoski at [email protected]). Voting membership requires sufficient participation in work group meetings.
The development of this voluntary standard will help influence the future design, acquisition and deployment of resilient PNT systems.
After the standards working group votes to approve the draft standard, it will be submitted to the membership of the IEEE Standards Association (IEEE SA) for final approval. The IEEE Standards Balloting Center will then send an invitation to any SA members it knows to be interested in the subject matter of the proposed standard, and anyone answering the invitation affirmatively will have a right to vote on the final standard.
Compared to the early days of GPS, PNT systems have become highly sophisticated pieces of equipment with a multitude of components, both hardware and software, along with associated vulnerabilities. Additionally, with a wide array of stakeholders and a variety of ideas on what PNT resilience means, getting consensus and developing such a standard would be challenging without an established process.
To help address this challenge, DHS developed the Resilient PNT Conformance Framework with input from industry stakeholders to establish baseline concepts on the definition of resilience and necessary behaviors within resilient PNT systems. DHS designed this framework to be outcome-based and non-prescriptive, to encourage industry innovation.
“To address security and resilience, GPS and PNT receivers need to be treated more like computers rather than radios,” said Ernest Wong, technical manager for the Science and Technology Directorate. “The refinement of the Resilient PNT Conformance Framework into industry standards will help to ensure that future PNT receivers are resilient and designed to withstand and recover from threats.”
Editor’s Note: This article does not represent a formal position of P1952 Working Group, Communications Society Standards Committee, IEEE, or IEEE SA.
UNMANNED SOLUTION, a South-Korean company based in Seoul, develops autonomous vehicles, including driverless shuttles, autonomous agricultural equipment, robots, and educational platforms. (Image: SBG Systems)
What is complementary / alternative positioning, navigation, and timing (PNT)? In this month’s cover story, five of our marketing partners share their perspective on this question and explain how their products address it.
The four global navigation satellite systems (GNSS), two regional navigation satellite systems and public and private augmentation services continue to provide exceptional levels of accuracy and reliability for positioning, navigation and timing (PNT). Yet their well-known vulnerabilities also continue to fuel the need for alternative/complementary sources of PNT data, especially for new and rapidly expanding user segments such as autonomous vehicles.
What constitutes a complementary service to GNSS for PNT and what constitutes a true alternative is partly a matter of definition and opinion. In a January report, the U.S. Department of Transportation stated
…suitable and mature technologies are available to owners and operators of critical infrastructure to access complementary PNT services as a backup to GPS. To achieve the parallel objective of resilience, as described in Executive Order (EO) 13905, that path should involve a plurality of diverse PNT technologies. Promoting critical infrastructure owner/operator use of those technologies that show strong performance, operational diversity, operational readiness, and cost-effectiveness is worthwhile. Based on this demonstration, those technologies are LF and UHF terrestrial and L-band satellite broadcasts for PNT functions with supporting fiber optic time services to transmitters/control segments. (Andrew Hansen et al., Complementary PNT and GPS Backup Technologies Demonstration Report, prepared for the Office of the Assistant Secretary for Research and Technology, Department of Transportation, January 2021, p. 195.)
A portion of the former USCG Loran Support Unit in Wildwood, New Jersey, with its iconic Loran antenna. (Image: UrsaNav, Inc)
For this year’s Q&A on complementary / alternative PNT, I asked five companies in the GNSS/PNT space to tell us how they define the issue, what solutions they prioritize, what markets they target, and which of their products specifically address the need to make PNT more resilient.
Roger Hart: The deep adoption of the state-sponsored, space-based global navigation satellite systems (GNSS) defines them as the primary PNT source at this time. Inertial navigation, long predating GNSS, does provide an independent navigation solution but does not provide time. In today’s conversation, alternative PNT generally refers to deriving position and timing from existing signals not purposed for navigation, to ground-based location systems, and also to emerging satellite systems that operate at higher power — or out of the GNSS band — to provide a diversity of PNT sources.
David Sohn: Simply put, alternative PNT is usually anything that is not GNSS. So, this includes PNT derived from low Earth orbit (LEO) satellites; vision, radar, lidar combined with inertial measurement units (IMUs) and map matching; positioning off cellular WiFi, digital TV signals and other signals of opportunity; legacy nav aids like VORTAC, ILS, DME and eLoran; and new dedicated infrastructure positioning systems like Locata, NextNav or RFID.
Matthieu Noko: Here at SBG Systems, for 15 years we have been developing navigation systems based on smart coupling of GNSS technology and inertial sensors. From our perspective, inertial sensors as well as sensors such as odometers or DVL, combined with high-end algorithms and RAIM, build consistent alternatives to GNSS-only systems in the vast majority of outdoor applications. Inertial sensors dramatically enhance GNSS-only navigation systems, making it possible to provide navigational data during GNSS outages in urban environments or to reject false GNSS measurements due to multipath effects.
As its entry into the driverless category of the Formula Student Germany car race, AMZ modified the car it has used in competition since 2015 to be driverless. (Image: SBG Systems)
The hybridization of several technologies makes it possible to reach a sufficient reliability level for the majority of commercial applications. However, in some indoor applications or in case of intentional jamming or spoofing, a higher resiliency is required. Using visual odometry can then complement quite effectively the navigation system, although this technology is still at a research level. Compared to infrastructure-based alternative PNTs — such as WiFi, Bluetooth or ultra wideband (UWB) ranging — visual odometry has the great advantage of not requiring any infrastructure.
Jacob Amacker: GNSS remains the dominant method of PNT in terms of market applicability and performance, but there are many rival technologies that have great potential and will compete with GNSS going into the future. The most important changes in PNT will be methods of position localization that are able to replace GNSS, and we will likely see these technologies integrated into huge systems, making use of ubiquitous technologies such as lidar. Whereas GNSS still provides the most accurate timing, as systems get more complex, time synchronization becomes a bigger issue, so different methods of this need to be explored.
There are many ways of improving the navigation data overall. Most commonly an IMU and a Kalman filter will be employed to stabilize any errors in the position localization method. A Kalman filter is a method of processing data from a range of sources—say, GNSS, an IMU, and a wheel speed sensor—and using them in such a way as to arrive at the position with a greater accuracy and precision than either source alone would be able to achieve. This process, however, requires precise timing for each data stream. Therefore, one area in which alternative PNT has to compete with GNSS is timing precision. GNSS makes use of atomic clocks used on satellites that are as accurate as you will get. There are also several ways of synchronizing time. A timing system can only be as precise as the most precise clock on the network, but there have been developments, such as Precision Time Protocol (PTP) that can synchronize timings across a network of clocks over Ethernet connections. Traditionally, PPS has been used and whereas this is still very precise it is not able to compete with PTP on convenience or sophistication.
Charles Schue: The common definition these days for “alternative PNT” seems to be with respect to, or as compared to, GPS or GNSS. Even the U.S. DOT’s website speaks to PNT as related to GPS.
I used alternative, complementary and backup somewhat interchangeably during my entire career with the U.S. Coast Guard. In recent years, I injected “co-primary” into the conversation as well. Prior to GNSS becoming ubiquitous, alternative, complementary and backup were not technology-based terms, but were instead operationally based. For example, “the prudent mariner” or “the prudent aviator” should use all means at their disposal to safely navigate their platform. For the navigator, this would include visual, audible and electronic signals or aids. The solution of choice obviously was the one that provided the highest accuracy, availability, integrity and continuity. However, prudence required always checking the solution of choice against other readily available alternatives, preferably that complemented each other, to ensure safety and continuity of operations. At one time, shipboard navigators might have at their disposal Loran-C, OMEGA, GPS, INS, radar, sextant, visual bearings (such as lights and landmarks), beacons, and soundings. Similar alternatives were available on aircraft.
Although always in the mix, timing was often in the background until around 2000. Then it started to become as important as positioning and, in many areas, even more important than positioning. Today’s incredible dependence on technology, and interdependence between technologies, means that knowing your “when” has become as important as knowing your “where”.
Whatever the terminology, the definition of alternative PNT should include some key features. Firstly, we should accept that the solution of choice today is GNSS, and we should define it as primary or co-primary. Next, we should acknowledge that when the primary solution is available and trustworthy, it should always be used, or at least considered. Finally, the primary solution should continually be compared with alternatives to ensure safe and secure provision of PNT to the user. Thus, an alternative PNT solution is one that is readily available; provides an easy and seamless transition to/from the primary or other alternatives; allows continuity of operation at a possibly degraded, yet usable, level of accuracy, availability, integrity or continuity; and is dissimilar enough from alternatives to withstand the effects that might be affecting the primary solution.
Do you agree with the U.S. DOT’s assessment, cited above, of what it will take to make the national PNT much more resilient and reliable? If you do, how do your offerings fit into that framework?
RH: While there are intricate differences in the signals generated by the primary PNT systems, they are all quite similar in terms of frequency and power and are all vulnerable to the same types of interference. Achieving the most resilient solutions will require the use of alternative RF bands and non-RF sources. Having a variety of alternative PNT sources will allow users to integrate the method most applicable to their platform constraints. Integration across the various PNT sources will need time synchronization to take full advantage of the alternate PNT systems. Our offerings work concurrently with GNSS, providing simulation and testing of GNSS and alternative PNT as true complements, while also offering the ability to measure timing accuracy in real time.
DS: Yes, we agree with the DOT’s assessment. However, to be clear, the DOT does not require “LF and UHF terrestrial and L-band satellite broadcasts for PNT functions with supporting fiber-optic time services to transmitters/control segments.” It stated that to achieve resilience, systems “…should involve a plurality of diverse PNT technologies…that show strong performance, operational diversity, operational readiness and cost-effectiveness.” Their demonstrations showed that those technologies they called out meet these criteria. Our solutions have been leading this resilient approach by offering several diverse, alternative PNT references.
We have fielded time-server equipment that operates from both GNSS and eLoran. Our standard offering time servers are equipped with multiple references from GNSS, network-based time services from NTP, PTP and PTP WR; internal references from disciplined atomic clocks; wireline references from IRIG, 1PPS or ASCII time code; and LEO PNT reference from the STL signal.
(Image: SimonSkafar_E+_Getty Images)
L-band or more generally the use of geostationary satellites was until very recently the only communication link for PNT augmentation services, although these signals are weak and easily disturbed or masked, especially at high latitudes. Resilient navigation will clearly need to allow multiple downlinks for corrections such as terrestrial networks (4G/5G) or satellite-based internet. In the mid-term, we expect the correction delivery over IP to become the standard, and L-band corrections to be used as a backup only. All our high-performance products already include an NTRIP client able to handle the IP corrections very easily.
JA: This is certainly one option. Largely, it is borne out of a need to compensate for the disadvantages of GNSS. This larger range of frequencies would provide a range of satellite-borne signals that have different penetration characteristics and information carrying properties but the same core purpose. Therefore, somebody making use of such a system will be better able to receive these signals even when under obstructions. Of course, some obstructions will still be impenetrable to GNSS signals and there is a long way to go to developing a comprehensive solution that can deal with timing differences when the signals travel through objects. It is likely that some other source of timing information, for example through the proposed fiber-optic services, will be necessary to smooth out these issues. Although we will see this much-needed upgrade to cover the shortfalls of GNSS employed, many other alternatives will start to take prominence. It is difficult to say which solution will win out, and it is likely that an upgraded GNSS will continue to dominate for the next decade or two at least. In terms of our offerings, we are exploring all possibilities and keep our core technology open to any position localization method. Of course, we will welcome any new technology that is a viable and improved method of PNT.
CS: I have long been an advocate of a system-of-systems approach simply because there is no PNT solution available yet that works everywhere, under all conditions, for all users, all the time. Many solutions provide only a component of PNT: an INS provides position (the “PN”), and an atomic clock provides time (the “T”). However, an INS does not know “where” it is without initialization and updates, and an atomic clock does not know “when” it is without initialization and updates. Fiber is awesome but is not wireless. Many alternatives depend upon GPS/GNSS as a necessary input. Others are augmentations that depend upon GPS/GNSS as inputs and not direct alternatives, such as space-based or land-based augmentation systems. Some are mode-dependent — such as VOR, DME, ILS, and TACAN for aviators — and thus not useful to other modes: time/frequency, maritime, land-mobile or handheld.
So, yes, we agree with the government’s assessment that low-frequency (LF), generally referring to eLoran in the United States, is the best, very wide area, terrestrial, wireless alternative, and is an essential component of any resilient PNT framework. Irrespective of whether the implementation is Loran-C, eLoran or LFPhoenix, LF is the lowest cost terrestrial PNT solution per million square miles of coverage. All our offerings are focused on the LF portion of the resilience framework. Our offerings easily integrate with any existing PNT technology and have proven in real-world government testing their ability to survive heavy jamming and spoofing environments.
What markets and applications do you target?
RH: Spirent Federal provides simulation test solutions to U.S. government and affiliated organizations. Applications range from core GNSS receiver development to real-time, hardware-in-the-loop system integrations. We have a long history of supplying the U.S. government and contractors with first-to-market products, from Y-code, SAASM, inertial and M-code, to sensor fusion of the latest alternative signals and sensors. We provide test solutions to safety-critical applications that are expected to have the same level of operational performance both in GNSS-available and GNSS-denied environments. Providing a single test platform that can help validate performance in both environments has received positive responses from users in the autonomous vehicle industry.
DS: Aerospace and defense, data-center and communication networks, public safety, industrial control, search and rescue, and space.
Autonomous self-driving mobility solutions move people and goods at appropriate speeds in urban and campus environments. (Image: SBG Systems)
MN: SBG targets a large range of applications including from a relatively small BVLOS drone for remote operation to large hydrographic vessels or airborne survey. We divide the applications into two main categories:
Surveying and mapping, where the inertial navigation system is used to stabilize the measurements from a lidar, sonar or camera to generate high-precision maps.
Control applications, where the PNT and orientation solution is used in real time to feed autopilot or to stabilize a camera. These applications include unmanned vehicles, machine control, camera pointing and more. High resilience is then critical to ensure safe navigation.
JA: Two main applications we are targeting with alternative PNT are surveying and ADAS systems. Both of these applications often make extensive use of lidar systems. We are therefore looking at lidar-based simultaneous localization and mapping (SLAM) algorithms to aid PNT or to provide relative position localization without GNSS. In cases when GNSS is totally unavailable, it is usually possible to set up ground control points. Although these cases are limited, they give much more flexibility in options. Anticipating a future where autonomous driving is the norm and not the exception, new building projects will need to be planned with the adequate systems in place to allow for them, and this will include a system such as UWB.
CS: Our employees have been involved in the design, development, deployment or sustainment of every Loran-C and eLoran system site in the world (transmission, control or monitor) since the mid-1970s, including components of the Russian Chayka system. Our service provider and end-user technologies are operationally proven in commercial and military environments. We specifically target the maintenance and upgrade of existing systems, as well as the implementation of new systems, globally.
Which of your products directly address the need for alternative PNT?
RH: In a broad sense, Spirent offers a market-proven and innovation-driven solution portfolio for the simulation of inertial sensors through the SimINERTIAL and SimSENSOR product lines, seamlessly integrated with our GNSS simulation. Spirent is actively engaged with several alternative RF vendors to incorporate signal simulation capability and will offer an alternative RF navigation product in 2021 called SimAltNav Replay. This product will allow for concurrent GNSS and alternative RF signal simulation. Additionally, Spirent offers many other alternative PNT solutions for testing resilient systems for connected vehicles and sensor-fusion algorithms for tactical and military-grade systems. We are developing new products to incorporate an open Ethernet interface allowing for open-source Ethernet-based sensor simulation.
Remotely controlled rovers are used to test and practice complex tasks in Mars-like desert environments. (Artist’s Rendering: Stocktrek Images_Stocktrek Images_Getty Images)
DS: Our time servers are equipped with high-quality precise internal time references such as OCXOs or atomic clocks and then disciplined by external references such as GNSS. They are resilient because they can operate precisely for long periods in GNSS-denied situations as standalone devices in holdover mode or from multiple alternative references, such as:
network-based NTP, PTP and PTP WR time services
wireline references from IRIG, 1PPS or ASCII time code
LEO PNT reference from the STL signal
eLoran when available
They are also resilient because they detect and mitigate interference from the GNSS signal before it can corrupt the PNT solution.
Our GNSS simulators are adding alternative PNT features to provide a complete test and evaluation solution for resilient PNT systems. We have recently added INS/IMU test features and have integrated with Anritsu’s cellular test stations to evaluate and qualify combined GNSS/cellular location functions. Orolia GNSS simulators support generation of custom GNSS signals and playback of IQ waveforms, and provide complete toolsets for GNSS jamming and spoofing testing. This allows creation of the threat environment to allow evaluation of alternate PNT signals as backup or alternative to GNSS. Orolia offers an open-source framework allowing any end user to develop their own sensor plug-in leveraging the Skydel simulation engine.
Our Resilient PNT for Defense product line includes the VersaPNT, which uses alternate non-GNSS PNT sensors such as IMUs, barometers, wheel ticks, INS and non-GPS-based LEO satellites. Alternate RF navigation or non-GNSS sources of radio frequency (RF) are of interest in highly degraded or contested signal environments. Interest is focused on low-Earth-orbit (LEO) constellations. These systems offer high receiver signal power (relative to GNSS) and a secure and resilient link to augment GNSS.
MN: All our products are designed to answer to challenging GNSS conditions, starting with our Ellipse series, which includes an industrial-grade IMU capable of coping with short-term GNSS outages. Its miniature size allows integration in robotics and also makes it suitable for cost-sensitive applications. Our Apogee and Horizon series, with their navigation-grade IMUs, are the most resilient systems in the event of GNSS outages. These products reach very high-end performance in real time, but become exceptional when used with our post-processing software Qinertia. Tightly coupled algorithms make the solution capable of coping with long-term GNSS outages.
JA: We have previously created solutions using retroreflective strips for path following with driving robots. and we are also compatible with Locata’s system, a large infrastructure solution popular for automation in shipping ports. More recently, we have released an offering for UWB in an integration with Pozyx. This is perfect for GNSS-denied environments as a direct replacement for what GNSS can provide in terms of position information. We are also exploring alternative ways to synchronize clocks and get timing information. This year we have developed PTP functionality on all of our devices. Alternative PNT is going to be vital as we look to the future of navigation and thinking about how we can navigate flawlessly anywhere and address more complex environments, particularly urban areas.
CS: We are focused on the provision of terrestrial low-frequency equipment and systems for primary, co-primary, alternative, complementary and backup PNT. We provide all the products and services required to design, develop, install, certify, operate and maintain Loran-C, eLoran and LFPhoenix equipment and systems. We provide the technology to perform coverage diagrams and site surveys; all the equipment required at a transmission site; all the equipment required at a differential reference station or quality-of-service site; all the equipment required for a monitor and control site; ASF measurement and analysis equipment; and various models of end-user equipment (including receivers and antennas) for the timing/frequency, maritime, aviation, land-mobile and handheld markets.
Soon, global navigation will no longer suffice. Humanity is preparing to return to the Moon after more than half a century. U.S., European, Chinese, Indian, Japanese and Russian governments and companies want a slice of the “eighth continent.”
NASA’s Artemis program, which aims to put astronauts on the Moon’s south pole in 2024, will explore more of the lunar surface than ever before. Robots and humans will search for, and potentially extract, resources such as water, which also can be converted into other usable resources, including oxygen and fuel.
Astronauts searching for spots where robotic spacecraft have pointed to the ice on the lunar map and for equipment sent on ahead of them will need precise navigation guidance. So will astronauts and ground controllers operating the Gateway outpost in Moon orbit and the Orion spacecraft. This will require extending the reach of our Earth-centric positioning, navigation and timing (PNT) systems to cover our planet’s nearest neighbor.
A permanent and reliable source of PNT on the Moon will reduce the amount of gear each mission will have to develop and carry, making more funding and rocket-lift capabilities available for scientific equipment. It also will free bandwidth on NASA’s communications networks, which have historically provided navigation services near the Moon.
NASA and the European Space Agency (ESA) are laying the foundations for this navigation system. Their efforts include the development of a special receiver able to pick up GPS signals that, already very weak on Earth, are extremely so on the Moon; NASA’s LunaNet communications and navigation architecture; ESA’s public-private Pathfinder satellite navigation and communication mission, due to launch into lunar orbit by the end of 2023; and ESA’s Moonlight initiative, which will establish lunar communication and navigation services.
Studies already have proven that it is possible to navigate between Earth and the Moon, as well as on the latter’s surface, using the side lobes of the signals from GNSS satellites. In 2023, the Lunar GNSS Receiver Experiment (LuGRE), developed in partnership with the Italian Space Agency, will demonstrate and refine this capability on the Moon’s Mare Crisium basin. NASA will use data gathered from LuGRE to refine operational lunar GNSS systems for future missions.
Besides the low signal power, other challenges to using GNSS satellites for Moon navigation include geometry, with all the signals coming from a relatively small portion of the sky; the fact that in polar regions the Earth would be low on the horizon and therefore GNSS signals could easily be blocked by hills or crater rims; and the complete occultation of the signals when moving beyond the side of the Moon always facing Earth. Meeting this last challenge will require at least a couple of Moon-orbiting satellites. (Artificial satellites orbiting our planet’s natural satellite as a supplement to the artificial satellites orbiting our planet…)
The Moon will be our steppingstone to Mars. I bet it will not be long before the Institute of Navigation establishes a Planetary Navigation division!
Alternative. Complementary. Backup. Co-primary. These are some of the terms used to refer to sources of positioning, navigation and timing (PNT) data other than GNSS satellites.
The four current GNSS constellations — supplemented by two regional ones and by public and private augmentation systems — have firmly established themselves as the primary source of PNT data by virtue of their accuracy, reliability, global coverage and ubiquitous use. Yet, this widespread dependency on them — especially on GPS — coupled with their well-known vulnerabilities to jamming, spoofing, other RF interference, multipath, solar flares and space debris (see page 10) — make the development of alternative sources of PNT data imperative. In fact, the U.S. Congress has repeatedly mandated it.
Typically, when talking about alternative PNT, we are referring to sources of PNT data that either were not originally developed for navigation purposes — such as television broadcast towers used as “beacons of opportunity” — or that use a higher broadcast power or a different frequency band than GNSS. They include legacy systems and new versions of legacy systems, such as eLoran.
“The only replacement for a GNSS is another GNSS.”
Other non-GNSS sources of PNT data have a wide range of benefits, limitations and costs, including infrastructure requirements. Most provide only the P and the N, or only the T, in PNT. Inertial systems, for example, once initialized can provide positioning and navigation, but need to be periodically re-initialized to compensate for their drift. Therefore, while excellent for maintaining the navigation solution during short GNSS outages and very helpful in identifying false GNSS measurements due to multipath, they are no replacement for GNSS. Cameras, radar and lidar, while often excellent sources of relative positioning, cannot provide absolute positioning.
It is even harder to replace GNSS when it comes to timing. Already enormously important in synchronizing the Internet, financial transactions and broadcasting, this service is essential to the development of complex new systems, such as integrating autonomous and legacy vehicles into digital traffic networks.
As in other human enterprises, the key to resiliency in PNT is diversity: a mix of systems based on sufficiently distinct technological foundations so that a threat to one does not imperil the other ones. Additionally, having a variety of available sources of PNT data will enable users to choose the ones most suited to their platforms.
However, we need to distinguish between technologies that can assist GNSS, such as inertial, and those that could substitute GNSS. I agree with Chuck Schue’s definition of the latter (see cover story, page 28): “an alternative PNT solution is one that is readily available; provides an easy and seamless transition to/from the primary or other alternatives; allows continuity of operation at a possibly degraded, yet usable, level of accuracy, availability, integrity or continuity; and is dissimilar enough from the primary solution to withstand the effects that might be affecting it.”
Ultimately, Schue pointed out to me, “the only replacement for a GNSS is another GNSS.” So, let us stop referring to systems that are not true substitutes for GNSS as “alternative PNT.” Complementary is a more appropriate adjective.
Last year, GPS World marked its 30th anniversary. That is a testimony to this magazine’s continued relevance, its commitment to its marketing partners, and its unmatched audited audience of 54,000 GNSS/PNT buyers, integrators and specifiers.
Taking on my new role on GPS World’s edit team was a homecoming of sorts because I began my current career a little more than 20 years ago as this magazine’s managing editor. I look forward to an ongoing conversation with many of you in the GNSS/PNT community — scientists, engineers, civil servants, uniformed service members, company executives and product managers. You may get an email message from me, and I will always welcome yours, at the e-mail address below.
“I look forward to an ongoing conversation with many of you in the GNSS/PNT community.”
Let me tell you a little about three passions that led me to this job.
Navigation has been one of my passions since I was a kid. When I was five years old, I lost track of my mother as she entered a store in Berkeley, California, and I kept walking down the street. It happened again when I was seven and had insisted on walking home alone from school in Milan, Italy. I was determined never to get lost again. So, when I was 11 and my family moved to Pisa, I was the only kid I knew who walked around — from school to sabre-fencing practice, to piano lessons, to my bus stop — studying a map and a compass. When I was 13, in shop class, I built a crude optical-range finder, based on trigonometry. Next, came the topo maps I used for hiking the hills and mountains of Tuscany. A few years later, as a graduate student at MIT, I began to sail around the Boston Harbor islands and off the coast of Maine. I learned to navigate using nautical charts, sextants, radio direction-finders, sonar, radar, Loran-C and, finally, GPS receivers.
Magazine journalism has been another one of my passions, since I co-founded a public policy magazine, Oregon’s Future, 25 years ago and became its editor. That was the first of seven editorial positions with magazines I have had over the past quarter century. Finally, my passion for public policy led me to degrees in political science and to my previous career as a research analyst — first for an independent research institute, then for state and local government. It gave me a solid grounding in public policy, statistical analysis and querying large databases.
So, this navigation enthusiast, policy wonk and experienced writer and editor is now in position to report on and advocate for the continuing growth and development of GNSS. I will also showcase new products and projects, and present facts and opinions from across the GNSS/PNT community.
This month’s cover story on autonomous vehicles is a perfect example. It confirms the central role of GNSS in one of the most significant technological advances we can expect to see in the coming decade — having vast implications for our society and environment — with facts and opinions from four industry leaders.
Autonomous vehicles are being tested both on open roads and in controlled environments. (Photo: Trimble)
The advent of autonomous vehicles (AVs) is one of three revolutions in the automotive industry that will likely change this country as much as cars did over the last century. The other two are the conversion from internal combustion engines to electric ones and the integration of cars into digital traffic networks.
Once mass deployed, AVs promise to dramatically reduce the number of traffic fatalities (42,000 in the United States in 2020, a National Safety Council report shows). They will never be sleepy, distracted, aggressive or drunk — nor will they engage in such inane human driving behaviors as texting while driving, playing chicken with bicyclists, or running red lights. They also promise to reduce fuel consumption, harmful emissions and traffic congestion by optimizing routes and increasing the number of people using car services instead of owning their own car.
To realize this vision, however, cars will have to do a lot more than just find their way on their own. They will have to perform flawlessly in an unpredictable world that includes toddlers, reckless drivers, fallen trees, sinkholes, construction and accidents.
Among the many sensors aboard an AV, the GNSS receiver has a unique role. It is the only one that can provide absolute positioning, in the form of latitude and longitude coordinates, to within a couple of decimeters anywhere on Earth. As such, it is “a key enabler to a lot of the vehicles to know precisely where they are and whether it is safe to activate autonomous systems,” says Gordon Heidinger, automotive segment manager, Autonomy and Positioning division at Hexagon.
A GNSS receiver cannot achieve the level of accuracy required for autonomous driving without robust corrections. Fifteen years ago, the state of the art was real-time kinematic (RTK) corrections. However, “the cost of that equipment exceeded the cost of a small car at that time,” recalled Steve Ruff, general manager, On-Road Autonomy Division at Trimble. “They were targeting a system cost of about $200. Today, that number is below $50, including the antenna, the GNSS positioning engine, and the software that runs on it.”
Today, all automotive manufacturers are using a form of precise point positioning (PPP) corrections, which is a one-way broadcast, as opposed to the two-way communication between a base station and a rover required for RTK. This means that a single correction stream can serve an entire continent, Ruff pointed out. “Once a vehicle is manufactured, we will support it with our PPP corrections stream for at least 10 years, which is the typical service life of a vehicle.”
Obstacles to Adoption
To achieve mass-market adoption, AVs will have to overcome numerous and complex obstacles:
The technical difficulty of dealing with a limitless number of unanticipated challenges, such as poor visibility because of weather conditions, unpredictable human behaviors, complicated obstructions, detours and potholes
The need to map millions of miles of roads, develop vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications, and protect vehicle software from hackers
The difficulty, if not the impossibility, of handing off control to a human quickly enough to be safe when the system is unable to deal with a complex situation
Questions about legal responsibility and insurance liability
Ethical dilemmas about how to program the system to respond in emergencies
The development of appropriate federal and state regulations
Resistance from paid drivers who fear losing their jobs, including 3 million U.S. truckers, and from many other drivers, who fear losing control over their safety.
Trimble has approached all the major car manufacturers, has several programs in development, and has received multiple positioning requests for information (RFIs), Ruff said. “In 2018, Trimble’s RTX corrections service was the first solution adopted for production use in passenger vehicles, providing absolute precise positioning for General Motors’ Super Cruise system.”
Additionally, Trimble is working with Qualcomm and with SiriusXM, which will deliver Trimble’s RTX corrections over its satellite network, just like it does with music. “It is a good partnership because about 80% of the vehicles in North America are coming equipped with SiriusXM radio technology,” Ruff said. “The OEMs do not have to buy any additional hardware.” RTX corrections can also enter a vehicle via cellular IP, L-band satellite broadcasts and, potentially, over a V2I link.
Hexagon has proposed a PPP solution for automotive, “mainly because we essentially have the world covered with base stations, and that is a hard thing to do,” Heidinger said. “We have been running a corrections network for a very long time.” PPP’s one-way broadcast offers better cybersecurity because the GNSS receiver does not have to disclose its position, he added.
Swift Navigation is building a global corrections network. To make it suitable for the automotive market, the company is aiming to make its corrections service affordable and scalable. “We realized quickly that neither of the traditional RTK and PPP approaches were going to meet those requirements,” said Fergus Noble, company co-founder and CTO, “so we invested in developing a corrections service pretty much from the ground up.”
RTK provides high accuracy and short convergence times but is typically costly to deploy because it requires a very high density of stations, Fergus explained. As a consequence, most providers do not have continuous coverage over a wide area. Conversely, while PPP is a true global solution, it is less accurate and takes a long time to converge. “That may be fine in a marine or land-surveying application, but not if you are driving through city tunnels and bridges and need it to be able to reacquire a high-accuracy position within a matter of seconds. Therefore, we took a hybrid approach, together with a lot of new IP that we developed.” The service provides coverage in all the United States and most of Europe, and is being tested in Japan, South Korea and Australia.
Accuracy and Integrity
A common target accuracy for lane-level positioning is 20 cm 95% of the time. That means that AVs need to know when their positioning accuracy falls beneath that threshold. “We are building into our positioning solutions an accuracy metric that is output along with the position information we are providing,” Ruff said. “[The metric] can be used by the intelligence in the system to decide whether it can rely on the GNSS solution or needs to switch to one of the other complementary technologies because GNSS accuracy is not fulfilling its lane discipline.”
Heidinger noted the importance of economies of scale when mass-producing vehicles, where cost and ease of manufacturing become factors. “We can take some of our high-end equipment and get you 2 cm of accuracy with this technology, but the price point and the feasibility of this going into mass production for automotive is not favorable,” he said. “So, we’ve taken the approach of providing a software positioning engine that can be fit onto any hardware.”
Hexagon is developing products in partnership with STMicroelectronics, using the company’s Teseo V family of measurement engines. “ST is one of the established leaders of automotive GNSS solutions,” Heidinger said. “We take their measurements and put our positioning and corrections solution behind that to give positioning with lane-level accuracy.”
Noble agrees on the importance of knowing the reliability of a vehicle’s GNSS-based lane accuracy. The prevailing approach, which fuses data from GNSS and other sensors, makes it acceptable for one data source to be temporarily unavailable if the system is aware of that outage, he said. “That is where you start to see Swift, and others as well, focusing on the notion of integrity.”
An AV’s level of autonomy determines its behavior during GNSS outages. For systems with Level 2 autonomy and below, the driver must remain engaged, while Level 2+ and Level 3 systems will alert the driver to retake control when needed. If a driver of a Level 2+ or higher system fails to reengage, the AV’s reaction depends on the system and manufacturer.
“When we start to see Level 3 or above self-driving systems come onto the market, they will require that the GNSS component has an ISO 26262 safety certification,” Ruff said. “Many companies, including Trimble, are going through, or have gone through, the process of safety-certifying their offerings. As part of the AV system’s safety architecture, they will build in the capability to safely curb the vehicle if the system detects a malfunction or a spoof or some other type of problem.”
Automation Levels
In 2014, the international Society of Automotive Engineers released a standard, adopted in 2016 by the U.S. National Highway Traffic Safety Administration, that classifies cars in six levels, ranging from Level 0 (no automation) to Level 5 (full automation, meaning vehicles that can handle the full spectrum of road and traffic scenarios without any assistance from the driver). While many production models already incorporate various forms of Level 1 driver assistance, no current production car exceeds Level 2, or partial automation, which requires the driver to monitor the vehicle’s surroundings and take over as necessary. No test vehicle has yet achieved Level 5.
Image: GPS World
Other Sensors
Beyond lane-level positional accuracy, safe driving also requires avoiding collisions with other vehicles in the same lane or straying into it. Cameras, lidar and radar will detect other vehicles as well as fixed infrastructure and random obstacles, measure their distance, and monitor their movement.
While lidar scanners are better than cameras as detecting sharp-edged features, such as curbs, cameras are better at detecting and interpreting visual cues, such as road signs and the location and curvature of lane markers. In bad weather, radar is essential, because radio waves, unlike light waves, can penetrate rain, snow, fog and even dust, enabling radar to “see” where cameras and lidar cannot. However, radar sensors cannot see much detail, and cameras do not perform well in conditions with low light or glare.
Besides providing data about a vehicle’s trajectory, inertial navigation systems (INS) also measure its attitude (roll, pitch and yaw), enabling the software to better correlate and interpret data from the other sensors.
For example, when a car brakes sharply, its front end goes down; any forward-facing sensors measure distances to points closer to the car than they did a moment earlier, when its chassis was parallel to the street surface.
INS can also detect unsafe conditions, such as excessive slip angle, which is the angle between the direction of the rolling wheels and the vehicle’s true heading. A slip angle as small as 0.5 degrees can trigger skidding, spins or rollover, especially in the case of SUVs and tall trucks. Wheel-speed sensors also help verify the vehicle’s movement.
“All these technologies have their limitations,” Ruff said. “However, if you design the system, including all these technologies, then you can come up with a robust, safe combination that will enable autonomous driving.”
In addition to helping to avoid collisions, these other sensors provide relative positioning by comparing the images they acquire with highly precise maps to help locate the vehicle, especially in urban environments, which are well mapped and rich in recognizable landmarks.
Imagine an AV moving through different environments. It might travel from a city with urban canyons that degrade GNSS navigation, yet with landmarks that help relative positioning, to a rural environment devoid of both. The AVs’ algorithms must constantly weigh how much to rely on the different sensors. “Many of the OEMs and car companies are seeing that even rain mist on a highway is very bad for lidar and cameras, because it creates a big blur, but that is where GNSS will perform really well because it is open sky,” Heidinger said. “So, the two types of sensor systems complement each other very well.”
“Odometry sensors, such as a wheel-speed sensors, minimize any potential drift and add robustness to data that may have a GNSS outage of greater than 5 seconds, such as longer tunnels,” said Wesley Hulshof, principal engineer – ADAS Testing at Racelogic.
Photo: Racelogic
Noble sees a split in the industry. Companies such as Waymo and Cruise are pursuing Level 5 autonomy and are “heavy users of lidar” as well as other sensors. Companies such as Swift are focusing on Level 2 and Level 3 series production vehicles. “If you are making a mass manufactured vehicle for the production market, it rules out using a lidar sensor,” Noble said. “It is just too costly and complex right now to use. So, typically, if you look at the systems that are out on the market today, such as a Tesla Autopilot or a GM Super Cruise, they are very reliant on the camera as the primary sensor. Obviously, also inertial and some use of radar.”
Maps and Communications
While accurate and up-to-date maps have an important role to play in making autonomous driving possible, the more detailed maps are, the more the world they describe is constantly changing.
Meanwhile, the sensors keep improving and dropping in price, making maps less important. In the end, AVs — like human drivers — will probably rely much more on their ability to “see” and analyze their environment moment-to-moment.
Also like their human counterparts, they will gain experience. Unlike human drivers, however, AVs will be able to instantly share their experience with every other vehicle in their area via vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications.
V2V communications will enhance safety by informing AVs of the trajectories of nearby vehicles. If a vehicle is speeding toward an intersection and not slowing for a red light, it will be communicating its position and trajectory to other cars over a V2V link, Ruff explained.
“Then your car can make the intelligent decision to pump the brakes and avoid that collision. The same positioning stack that operates as part of the AV stack can also be used to support V2V-type applications, and the position of the vehicle will be much better than what the current V2V spec states.”
Different Approaches
Each GNSS manufacturer is taking a different approach to AV positioning.
The worlds of traditional automotive positioning and the products on which NovAtel has historically focused are coming together, Heidinger said. “The autonomous technology is demanding it and pushing for higher performance and safety-of-life functionality. Hexagon is bringing high-performance positioning solutions to the automotive industry in a manner that accepts automotive manufacturability, quality and efficiency.”
The company has also joined the 5G Automotive Association (5GAA), a large consortium developing AV solutions. “There are probably 100 companies in the industry coming together and helping to develop that vehicle-to-network communications solution, including telecom partners and automotive partners, and we are providing the GNSS expertise,” Heidinger said. “To meet the high-volume production-intent applications, including automotive quality, we recently developed a receiver based off the ST Teseo V family of measurement engines. We have an ST Teseo V set of chips on the PIM 222A product that launched in May geared exactly toward the automotive market.”
By contrast, Trimble is not focused on providing GNSS receivers or other hardware. “We allow the Tier 1 automotive manufacturers to architect the system using the components that they have selected from their preferred suppliers,” Ruff said. “We tailor our positioning solution to work with their architecture. So, we are agnostic as to the selection of the GNSS receiver, the IMU, the operating system running on the host system, and the host processor that runs the software. We can adapt our stack to run on virtually any system, using measurements from any GNSS source that meets our API requirements.”
For Swift, its “vision from day one has been to bring this type of precise positioning technology to mass market applications, such as automotive, which is a big focus for us,” Noble said. “That includes autonomy, but also ADAS, HD navigation and V2X. We do not want to be a hardware supplier in the automotive supply chain. Our boards are focused on professional and industrial markets.”
Swift’s automotive software, called Starling, runs on the vehicle’s computer. To generate a precise position, it ingests raw sensor data, as well as corrections data from the company’s Skylark network. “We focus on providing a precise-positioning stack that layers on top of any of this current generation of low-cost, automotive-grade receiver hardware from companies like STMicroelectronics.”
This test in London shows the value of inertial and wheel speed sensors. (Image: Racelogic)
The Future
Speculation abounds as to when AVs will enter mass production and how the transition from human to robotic drivers will take place. “There might be a ‘classics only’ lane in the future,” Heidinger said “that will be the only place where cars are allowed to be driven manually.”
Safety-enhancing automotive devices typically start out as optional extras, then get incorporated into best-practice standards promoted by independent bodies. Eventually, they become compulsory.
Some automakers have committed to creating their own AVs, while others are intent on creating a turnkey solution to transform conventional cars into driverless models. However, the initial market for AVs likely will be commercial fleets rather than individual consumers.
“It will still take quite a few years before we see cars take over and drive themselves, because legislation, insurance and these sorts of things will have to happen along with the technological advances,” Heidinger said. “But the positioning side is becoming more defined. We are seeing things like L5, the Galileo constellation, coming in and becoming more available. There are more constellations providing more data for use in our solutions, so that is promising.”
Swift’s Noble said, “Most of the major manufacturers working on Level 2+ and Level 3 systems are realizing that precision GNSS will be a key component of their architecture. Most of the major OEMs have signaled some level of intent to integrate this technology. Most are tracking to start the program next year,” he added.
“We envision that in five or six years every vehicle will have a single positioning utility on board that will serve all the location-aware applications on the car — whether it is an autonomous vehicle, V2V or V2I,” Ruff said. “It will meet the most stringent accuracy requirements from all the applications and serve navigation, telematics, security, V2X and AV/ADAS applications.”
A test of Racelogic’s parking assistance system. (Photo: Racelogic)
Racelogic helps vehicle manufacturers develop autonomous vehicle technology and test them on indoor test tracks and the open road.
Racelogic helps vehicle manufacturers develop autonomous vehicle (AV) technology and testing houses test them. Over time, regulatory and consumer testing has evolved from indoor test tracks to outdoor open-road tests, and then to indoor controlled test environments.
“Due to their application, advanced driver-assistance systems (ADAS) originated and are still mainly developed and assessed on open-sky, controlled test tracks, tackling the most common killed and seriously injured (KSI) accident types,” said Wesley Hulshof, principal engineer – ADAS Testing at Racelogic. “These assessments usually make use of sophisticated driving robots for closed loop, centimeter-accurate path following and precise speed-controlled test-track assessments. The robots can only attain this accuracy by being fed the speed and positional data by GNSS sensors, such as the Racelogic VBOX.”
Accuracy is key to conducting assessments for the European New Car Assessment Programme (Euro NCAP) and the U.S. National Highway Traffic Safety Administration (NHTSA). Using GNSS in conjunction with RTK base stations provides centimeter-level accuracy in position, said Hulshof, as well as accurate speed and heading information to measure ADAS data to both static and moving targets. Additionally, combining a GNSS receiver with an inertial measurement unit (IMU) allows for low-drift, high-accuracy speed and positioning information within areas of high GNSS multipath or temporary occlusions, such as gantries, bridges, forests or built-up areas.
However, “people do not just drive on closed test tracks with accurately positioned targets and infrastructure,” Hulshof said. “They do not drive at a constant throttle position and maintain an exact time-to-collision to the vehicle in front of them, like robots do. In fact, people often drive erratically.”
For these reasons, testing houses are conducting supplementary assessments on the open road, under real-world conditions. In these conditions it is still important to know vehicles’ positions and speeds to localize them and validate the system’s sensors, networks and algorithms.
Testing Stages
Stage I: Controlled
ADAS was developed for outdoor use because this is where car crashes occurred. For this, an open-sky GPS signal was essential for positioning. The types of tests and level of scientific rigor meant that the tests could be performed on closed test tracks.
Stage II: Randomized
Tests were brought to the open road to add elements not found within a closed environment such as traffic and higher speeds of the vehicle under test. For this, extra sensors were employed to add robustness in areas of obscured GNSS coverage.
Stage III: Controlled
Testing is brought back indoors for climate control and to assess L3/L4 AD functionalities such as valet parking.
Because open-road testing does not permit being constantly within range of a static base station, Racelogic developed a moving base solution for open-road testing that gives accurate relative positioning between two or more vehicles.
The increased demand for real-world testing of ADAS has generated demand for reliable ground truth data. “For example, if you consider a car driving on the winding roads of the Italian Alps and the position is out by 2m,” Hulshof said, “that is the difference between lovely scenery and falling off the side of a cliff. So, you need centimeter-level accuracy in the positional algorithms of the self-driving car, but also in the assessment tools, while we are testing it. For that reason, we still need GNSS and would ideally need RTK.”
To meet this demand, Hulshof said, Racelogic produced its own networked transport of RTCM via Internet protocol (NTRIP) solution, consisting of a modem and associated service provider. It allows for global coverage of high-accuracy, absolute positioning of a test vehicle in open-road conditions. Both the NTRIP and the moving base solutions allow ADAS testing to centimeter-level accuracy on the open road without the need to be in radio range of an RTK base station, thereby greatly expanding the testing possibilities.
“Whilst both the NTRIP and the moving base options allow for high-accuracy positioning,” Hulshof said, “they are still reliant on having an open sky for good GNSS coverage. IMU integration allows for improved accuracy over short periods of occlusion, but to truly give as accurate a signal as possible we need to be open to accept information from multiple satellite sources. That is why highest longevity accuracy is only achieved by using the GPS, GLONASS, Galileo and BeiDou constellations to provide the best RTK positioning performance in areas where that was not previously possible.”
To control the environment and allow for year-round testing, test laboratories such as the Insurance Institute for Highway Safety (IIHS) facility in Arizona and Asta Zero in Sweden have purpose-built covered test facilities, giving shelter from extreme heat or cold. Testing inside both set-ups, however, still relies greatly on the test vehicle positioning. Standard positioning techniques via GNSS in these situations is simply not possible. Therefore, Hulshof said, Racelogic designed the VBOX Indoor Positioning System (VIPS), which allows for seamless testing indoors or outdoors. “Because this system works as an alternative to satellites, with the in-vehicle VBOX allowing RTK-level performance without GNSS, the test vehicle can travel from open-sky outdoor testing to a closed environment seamlessly, with no drop in data during the transition or afterward.”
Finally, Hulshof said, ADAS and AD systems have moved on from straight-line highway scenarios to low speed turning scenarios often performed away from the open sky previously required for accurate GNSS coverage. Examples include multi-story parking garages and valet parking. “Scenarios such as self-parking and park-assist assessments, as well as indoor L1 ADAS, are becoming increasingly common requests by manufacturers on test facilities.”
These environmentally controlled facilities can simulate real-life conditions that affect specific sensors — such as sensor flare, fog, mist and water films. These types of facilities use VIPS to give outdoor GNSS accuracy in an indoor controlled environment. “There is a trend toward bringing the testing from closed test track to randomized real world back into a highly contained, climate-controlled area,” Hulshof said. “We then have an option for anything.”
Are military tests that jam and spoof GPS signals a threat to the safety of civil aviation? If not, why? If so, who should do what about it?
Bernard Gruber
“I would offer that military tests that jam and spoof signals are a risk. The U.S. military takes great care to control tests of this nature in an informed and careful way in order not to affect civil aviation. I cannot speak for military tests that are conducted by other countries. We all recognize the worldwide proliferation of small and large jammers that can negatively affect GPS performance and satellite-born transmissions. Accordingly, GPS users should remain vigilant to these potential hazards, including spoofing, and consider alternative navigation means where risks dictate.” — Bernard Gruber
What are the remaining obstacles to creating a seamless indoor/outdoor positioning and navigation system that integrates data from GNSS, inertial guidance, indoor positioning systems, and signals of opportunity?
John Fischer
“The primary use case for indoor navigation is the smartphone. We can create multi-sensor navigation systems today that operate indoors, but not at the very small size, weight, power, and cost targets needed for the personal phone market. IMUs and processors continue to improve over time, so there may be a breakthrough there, but signals of opportunity (SoOP) navigation is promising and offers resiliency through diversity. The most ubiquitous SoOP is cellular and with ultra-reliable low latency (URLL) features coming on-line for 5G in the next few releases, we may see reliable positioning from 5G in indoor environments very soon.” — John Fischer
The worlds of UAVs, lidar and surveying overlap, with UAV-based lidar able to shed light on places that are difficult or dangerous to access by other means.
Two questions come into play when deciding whether to use UAV-based lidar for a surveying project. First, do you use a UAV or a manned aircraft? The answer concerns cost, safety and efficiency.
Second, do you use only photogrammetry or photogrammetry plus lidar? This answer depends not only on cost, but payload weight — the single biggest constraint with UAVs. Lidar scanners weigh considerably more than comparable digital cameras.
Far from being mutually exclusive, photogrammetry and lidar are complementary, because digital images make it possible to colorize lidar point clouds, making them easier to interpret. However, the less a UAV’s payload weighs, the greater its flight time per battery charge.
“Most surveyors do not want to be UAV pilots. They want to do their job faster and easier,” said Jake McCay, director of business development at Lidar USA. His company manufactures laser systems — integrated with IMUs and software — for backpack systems, UAVs and helicopters. UAVs make surveyors much more productive and yield more accurate data because they enable them to collect many more points, he said.
UAV versus manned aircraft
Traditionally, data for corridor mapping — such as for power lines and railroads — has been captured with helicopters. However, cost and safety considerations have increasingly shifted the balance toward UAVs, especially hybrid systems that can take off vertically then transition to horizontal flight.
UAVs are also able to fly much lower than manned helicopters, thereby capturing data at much greater resolution.
Nevertheless, manned aircraft still have advantages. “Typically, the break-even is somewhere between 20 km and 40 km on a corridor mapping project if you consider a multi-rotor setup,” said Philipp Amon, business division manager, ULS, Riegl Laser Measurement Systems GmbH. “It takes a week of data acquisition using a UAV and two staff out in the field for what you can normally collect in half a day using a manned aircraft. The costs are almost the same.”
Beyond-visual-line-of-sight (BVLOS) flights are challenging for UAV pilots, because it makes them nervous to lose sight of their expensive platform. Successful BVLOS flights require a dependable and redundant data link. High-quality video transmissions that allow operators to monitor their UAV’s behavior in real time and with no significant latency are also very helpful. “If you do not have all these systems in place, I would not risk it either,” Amon said.
Whether mapping a corridor with a UAV or a manned helicopter, it is best to fly in one direction to the side of the corridor, then return on the other side, capturing data at an oblique angle rather than at nadir. This doubles the point density, enables the correction of any shadows created in a single flight, and — in the case of power lines — enhances safety.
Manned operations require a team of four and a helicopter, as well and a much greater focus on safety than UAVs, said John “JP” Cannon. Cannon is a UAV pilot for PrecisionHawk and team lead of the company’s lidar flight operations, totaling five pilots and more than 10 lidar sensors.
With a manned aerial survey, “You are a little more efficient, but you are burning a lot more logistics to get to that point,” he said. With a UAV, “if you have a properly calibrated sensor and a well-trained pilot, you can get even better data because you can fly lower and slower.” A manned helicopter would require multiple passes to get the same quality of data.
UAVs can collect data even in very remote locations, for later post-processing. (Photo: Lidar USA)
Lidar and photogrammetry
“We combine our lidar systems with all kinds of photogrammetry solutions, such as standard RGB cameras, in both nadir and oblique mounting options,” Amon said. “We also have multi-spectral cameras, hyperspectral cameras, and thermal-imaging sensors in our portfolio, and we offer fully integrated systems that combine all these sensors into one system.”
His customers prefer to use lidar sensors, especially to penetrate vegetation, Amon said. “That is often the most critical part of a survey, especially if you have dense vegetation and are looking for small objects, like in a powerline survey.” While a laser scanner’s multiple returns make it possible to extract surfaces even under vegetation, photogrammetry excels for spot detection.
“If you really want to nail down the error at a specific point, you will need to look at the photogrammetry data. If you want to do surface extraction, classification and remove vegetation, then you are looking for lidar.”
It is generally much faster to post-process lidar data because it does not require georeferencing and correcting thousands of images, but extracting and classifying features takes about the same amount of time.
Lidar “enables utility industry leaders to more effectively manage their networks,” said Cannon. It gives them “a visibility of their assets that photogrammetry just cannot provide, with more robust, precise and consistent data sets.”
Lidar data, he argued, is also less labor-intensive than photogrammetry, because the latter requires constantly tweaking camera features to deal with changes in the environment, such as the amount of light, whereas a well-calibrated lidar scanner “always performs.”
After having tried numerous lidar scanners over the years, PrecisionHawk chose the Riegl miniVUX-1DL, a downward-looking version that can shoot 23˚ off nadir, forward, center and rear. “We use it 20 times a day across multiple platforms.,” Cannon said. “Its data output is consistent and reliable.”
Dissenting voice
A dissenting voice is that of Wingtra, a manufacturer of vertical take-off and landing UAVs for mapping, survey and mining industry professionals, which has decided not to pursue UAV-based lidar for surveying. “We looked at different use cases, which sensor makes sense for each one, what is already there, and what can be done with manned aircraft and photogrammetry,” explained Andrea Nater, the company’s customer success manager.
“We found that the space for UAV-based lidar systems is very small. There are claims about very high accuracy, but we have not seen that. The point density we have seen so far is limited to 10-cm spacing, so you are really limited in an accurate and dense point cloud, whereas you can have a much higher resolution with photogrammetry.”
While the platform’s absolute position is independent of whether it carries a digital camera or a lidar sensor, “if you have fewer points on the ground, you also have less accuracy,” Nater said. For large areas, UAV-based lidar cannot compete with manned aircraft carrying expensive systems, she said.
“We have also compared manned aircraft with a UAV with low-cost lidar and an RX1 camera. For most use cases you are better off with a high-quality camera rather than a ‘low cost’ lidar. Despite the lidar being more expensive than the camera, the final outputs (point cloud or 3D mesh) generated by photogrammetry have a lower noise level and a higher point density.”
As a bonus, there are more tools for photogrammetry. “The workflows with the many photogrammetry companies are very simple to use, whereas for lidar it is still not as well established and easily adoptable by everyone as it claims to be,” Nater said.
Wingtra’s UAVs perform vertical take off and landing (VTOL), but fly horizontally. New European regulations easing restrictions on flight beyond visual line of sight (BVLOS) make this increasingly common. (Photo: Wingtra)
Positional accuracy
Achieving high positional accuracy with a UAV is challenging, due to the platform’s weight and size limitations for GNSS receivers and antennas. For dedicated UAV missions, Riegl uses the Applanix AV14 and AV18 antennas. The latter can acquire corrections directly from the satellites on L5 without needing a base station, achieving an accuracy of about 5–10 cm.
“We mainly couple our systems with Applanix APX-15 UAV or APX-20 UAV INS/GNSS components,” Amon said. “There are almost no cables needed for an overall system set-up besides power and GPS.” To achieve accuracies of a couple of centimeters, Riegl recommends that users post-process the data. Nearly all of them do, using a single base station in addition to the L-band corrections.
PrecisionHawk uses Riegl lidar equipped with the Trimble Applanix APX20 IMU for direct georeferencing of collected points. “It gives us an absolute and relative positional accuracy of about 2 cm to 5 cm horizontally, with a little bit less vertical accuracy, from 8 cm to 10 cm,” Cannon said. “We couple it with our NovAtel base-station data for PPK corrections. So, everything we do is post-processed, which enables us to focus on safety and efficiency in the field, rather than, say, pulling in RTK corrections and constantly stopping due to jammed signals.”
Lidar USA uses GNSS receivers from “pretty much every manufacturer,” McCay said. “What system we choose depends on the client’s specs. The performance varies greatly. You can buy a $5,000 GNSS-IMU or a $180,000 GNSS-IMU.” Likewise, Lidar USA is not married to a specific platform. “Our system is universal and can be put on several different platforms, as long as they have the payload capacity and have enough clearance for the system underneath.”
Lidar can reveal the intricate details of an infrastructure, such as this power plant. (Photo: PrecisionHawk)
Multisensory systems
The most common combination of sensors is lidar and RGB. Recently, however, demand for multisensory systems has increased Amon said, especially using hyperspectral integrations and multispectral cameras. “We are using well proven consumer-grade Sony cameras as well as thermal cameras such as the FLIR Tau 2.” The exact mix depends on the customer’s application.
While Riegl sells lidar sensors for customers to use in their own integrations, it also sells complete systems, especially lidar sensors coupled with Applanix INS/GNSS systems and complete turnkey solutions using the systems combined with a platform such as its RiCopter UAV platform.
“We also offer specialized integration kits for the most common UAV platforms, such as the DJI M600,” Amon said. The company also provides software libraries for self-integration, as well as its own data acquisition and postprocessing software.
PrecisionHawk couples its Riegl lidar scanners with Sony A6000 cameras for a dual RGB collection, enabling the company to generate colorized point clouds.
From Nat Geo to Bigfoot
“We have done all sorts of cool projects, from flying for National Geographic in Mexico to looking for Bigfoot in Oregon,” Cannon recalled.
A project for the largest utility provider in the South that has been ongoing for two years involves collecting hundreds of miles of distribution lines across an entire state, including a complete inventory of all the poles.
“These poles have been put up for 100 years. They get put and up and taken down every other day, due to storms and so forth, so who knows what is out there and how accurate it is? Some of the maps they have are from the 1980s.”
Besides accurately locating the poles, the project involves cataloging the assets on each one, such as AT&T equipment, as well as vegetation encroachment and sagging lines between poles. PrecisionHawk executes an average of 25 flights a day for the project, collecting more than one terabyte of lidar and RGB data each month. The data is analyzed using PrecisionAnalytics software.
Lidar USA recently scanned a remote open pit mine in Montana to assess elevation changes from gravel runoff. “There was no cellphone service, and the closest town was probably an hour away,” recalled McCay. “Even in that environment, it is amazing how well our system can perform. The most challenging aspect was that the mine was between two mountains and there were extremely high winds. At one point, the UAV went sideways. Fortunately, our pilot was very experienced, so he was able to correct for that.”
Centimeter-level positioning and high-accuracy orientation of machinery enable automation of many construction, mining and farming tasks, and take them one step closer to being performed by autonomous machines. Machine control increases jobsite safety, operational efficiency and productivity.
Using data from GNSS satellites, total stations and 3D models, machine-control hardware and software solutions determine a machine’s current position on the Earth and compare it with the desired design surface, mining task or cultivation technique. They also monitor and sometimes control the position and orientation of implements — such as blades, buckets and seeders — with respect to the machine. By talking directly to the machine’s hydraulics, machine automation shifts responsibility for accuracy and speed from the operator to the technology.
On construction sites, automation guides motor graders, excavators, dozers and other heavy machines, making operations easier to manage. This makes contractors more productive and experienced operators more efficient. With this technology, less experienced operators are able to take on more complex tasks, and all operators become more accurate. Machine automation also increases the capabilities of the machines themselves, so that excavators and compact machines are now doing finish grade work once reserved for larger and more expensive dozers.
Operators in the cab and engineers and supervisors at their desks can control and monitor progress in real time, with views of the whole layout as well as specific slopes, roads, ditches and other elements, including those under water.
Using GNSS guidance to aid application of fertilizer, pesticides and herbicides saves time and money. (Photo: Septentrio)
About half of all motor graders and a third of all dozers use positioning sensors and a display to provide operators with the position of the blade with reference to the target grade. A typical machine control set-up consists of a GNSS receiver and a display (jointly referred to as a “cab kit”) and inertial measurement units (IMUs) on the blades and other implements.
From the display, the operator loads a project design, which tells the system the cut, fill and other design information it needs. The operator then chooses a lane and may choose a vertical offset, which temporarily adjusts the design grade, making it possible to accomplish the work in steps, from rough to finish grading. Operators can also record points and scan a pavement in real time as they repair it.
While used by the construction industry on earthworks equipment since the late 1990s, machine control has recently benefited from:
The increase in the number of GNSS signals available, particularly on the new L5 frequency
IMUs, which measure blade movements with respect to the machine 100 times per second, one order of magnitude more than non-IMU grade-control systems
The growing availability of continuously operating reference stations (CORS) and other GPS networks, which eliminate the need to set up a base
New mastless systems, which integrate a receiver into the top of the cab and connect it wirelessly with IMUs to orient the blade, obviating the need to install a long mast pole on the blade and connect it by cable to the receiver and improving safety, visibility and equipment durability
New interfaces designed to be as easy to use as a cell phone, shortening the operators’ learning curve.
While these developments are hastening the advent of autonomous construction, mining and farming machines, remaining barriers to this vision include hardware and software issues as well as questions of data exchange, legal liability and operator training — issues analogous to those facing the development of autonomous cars and trucks.
The DINO is a one-ton farming robot made by NAIO Technologies that operates autonomously using GNSS positioning and maps for navigation. Of the 170 NAIO farming robots currently in operation, about 30% are DINOs, which are typically used on large farms.
In 2016, NAIO and Septentrio, a manufacturer of industrial high-end GNSS technologies, began to research the integration of full GNSS solutions into NAIO’s robots.
Today, the DINO carries a Septentrio NR3, consisting of a GNSS receiver and antenna in a single housing, which provides it with RTK centimeter-level positioning accuracy. Farmers can use the NR3 to map their fields, then attach it to the DINO to guide it.
The DINO automates weeding within complex and quickly changing environments. NAIO plans to soon add seeding and fertilization to its robot’s capabilities.
To operate reliably in the narrow lanes between crops, the DINO requires an accurate GNSS receiver with strong resistance to multipath and jamming.
The safety of field hands and the protection of the crops also require the receiver to have good integrity, which is a measure of the trust that can be placed in the correctness of the information it supplies. Accuracy, robustness, and integrity are all strong suits of Septentrio’s NR3.
While the DINO mostly operates continuously, it sometimes stops to avoid animals or humans, or for other safety reasons. A major advantage of the NR3 and other sensors that NAIO is using, is that they enable the robot to perform cold-starts very rapidly and with a stable heading.
Machine control, guidance and automation defined
Using GNSS guidance to aid application of fertilizer, pesticides and herbicides saves time and money. (Photo: fotokostic/iStock/Getty Images Plus/Getty Images)
The terms machine control, machine guidance and machine automation are not interchangeable.
Machine control is a generic term that refers to the integration of positioning tools into a construction, mining or farming machine to determine its position on the Earth and relative to a desired design surface, mining task or cultivation technique.
Within machine control, machine guidancesystems display these data in the cab — assisting the machine’s operator in steering the machine and in maneuvering its implements to shape the ground, mine minerals, plant seeds or perform other related tasks — while machine automation systems directly steer the machine, achieving greater levels of precision than human operators could. The term automated machine guidance (AMG) is sometimes also used.
Caterpillar’s Cat Command system enables operators, including disabled veterans, to control machines in dangerous environments from the safety of a remote command center. (Photo: Caterpillar)
Caterpillar, the world’s largest manufacturer of construction equipment, has invested in the development of autonomous vehicles for more than 30 years and has the world’s largest autonomous fleet of haul trucks.
Its Cat Command suite of remote and semi-autonomous products for the construction industry helps increase safety, machine utilization and productivity for hauling, loading, excavating, drilling and dozing operations. They include onboard electronic and vision systems that allow machines to be controlled without anyone in the cab.
Options include
The line-of-sight Cat Command Console, which is supported by a shoulder harness
The Cat Command Station, which can be located onsite, for line-of-sight operation, or offsite
The semi-autonomous Cat Command for Compaction technology, which automates soil compaction to help deliver consistent results.
Over time, the company expects most of its machines to become compatible with its Cat Command technology.
Here are a few examples of how construction companies are using Caterpillar technology:
Cargo Barges. Associated Terminals, which transloads dry bulk and general cargo in the Port of South Louisiana, uses Cat Command to remotely control its small wheel loaders and excavators, keeping its personnel off the barges.
“It gives me a lot of peace of mind knowing that when we are doing our jobs, digging in these cargo holds in the vessels, my friend and co-worker is not operating the machine in the hold,” said Thomas Ramagos, a production manager for the company.
Fleet Management. Beverly Companies is a landscaping, snow removal and topsoil contractor in Chicago that owns equipment ranging from bulldozers to lawnmowers. The company uses my.cat.com and other Caterpillar fleet-management tools to track all its equipment in one place, help reduce machine downtime, manage repairs and maintenance, and order parts.
Civil Contracting. Saiia Construction Company, a civil contractor in Birmingham, Alabama, uses Cat Command to increase the safety of its employees, said Frank Montgomery, the company’s president. The material with which it deals is sometimes unpredictable, and rain events can change conditions significantly, explained Superintendent Clint Kennedy.
A remotely controlled front loader operates inside a barge. (Photo: Caterpillar)
Cat Command enables employees to work from an office trailer, rather than having to trudge through mud and muck to get to a piece of equipment. The controls in the seat are almost identical to the ones in the cab, Kennedy pointed out. Another employee can stand behind the chair and coach the operator.
High-quality cameras on site enable the operator to view the whole job site, while four on the machine enable the operator to distinguish brown dirt from red dirt and rocks from sand.
Caterpillar machines also collect massive amounts of data and transmit them over the air to the company, where they are analyzed and used in business applications.
Customers can access these data via my.cat.com and a mobile app to better understand and manage their vehicle fleets and operations, reduce fuel consumption, and improve productivity and safety. They can also access equipment locations, engine hours, parts and service records, and inspection reports.
According to Caterpillar, it had one million connected assets at the end of 2019, almost twice as many as three years earlier, and almost all its new construction machines are equipped with these connectivity systems. The Cat Productivity web-based suite of solutions works with Caterpillar machines of any age and brand. Of course, newer machines will provide richer data and more accurate results.
