Paul McBurney

The need arises from attacks directed by bad actors on a large scale, such as electronic warfare, and on a more humane scale, by bad actors such as self-jammers and spoofers. On top of that, normal interference can cause desensitization and denial of service on GNSS receivers from myriad terrestrial and satellite communications.

The PTA plan presents the Denial Radius Reduction Ratio (DRRR) figure of merit and shows that a J/S increase of 15 dB produces a DRRR of 0.18. Whereas a receiver without this additional 15 dB of J/S could be denied fixing out to 1 km from a given transmitter, a receiver with an additional 15 dB J/S would be denied out to only 180 m from the same transmitter.

The improvement in terms of area is proportional to radius squared. The article identifies that the J/S capability is different among GNSS signals and the best performance is obtained with L5, mainly because it has the highest chipping rate. L1C has a code length of 10,230 chips, the same as L5, but it is spread over 10 msec and has the same chipping rate as L1 C/A.

There are currently 72 L5 signals between GPS, Galileo, BeiDou and QZSS transmitting the same physical layer features of 10.23 MHz chipping rate, 1 kHz overlay codes and higher transmit power compared to nearly all L1 signals with a 1.023 MHz chipping rate and lower transmit power. The combination of these features at L5 is close to achieving this 15 dB performance level over L1.

Unlike current hybrid receivers, L5-first survives L1 jamming. (Photo: Carkhe / iStock / Getty Images Plus / Getty Images)

One might conclude that the current start of the art of a receiver with both frequencies (aka, a hybrid L1+L5) has this resilience. However, the market does not currently offer the ability to directly acquire L5 signals overall use cases of GNSS assistance without first acquiring signals at L1. This means they can only achieve this resilience when the interference is encountered after acquiring and fixing at L1. As soon as the L1 is lost and the position and time uncertainty grow beyond the receiver’s capacity to autonomously search for L5 signals, the receiver is denied service at the interference level tolerable at L1. If you cut the receiver into L1 and L5 pieces, only the L1 side is capable of fixing autonomously. As noted by Dennis Akos et al. (“Testing COTS GNSS Receivers Using Only a Subset of Supported Signals,” ION JNC 2023), “support for several signals/frequencies provides integrity and robustness.” Specifically, “under jamming scenarios, signal diversity can allow a receiver to still generate an accurate position solution.”

Current receivers are not able to acquire L5 for reasons related to history, cost and power consumption. Historically, the promise of L5 accuracy was so attractive that it was added to legacy chipsets based on L1 even when it was only partially deployed. It was impractical at that time to require L5 acquisition when there were fewer L5 satellites than at L1. Cost and power are related to the fact that L1 receivers’ acquisition methods are sized to acquire the L1, E1, B1 and G1 signals. Memory and compute capacities, including the digital clock speed, are sized for slower chipping rates and hence shorter code lengths. At this performance level, conventional time domain correlation is adequate. Some receivers deploy frequency domain methods at L1 and achieve a lower cost and power than time domain methods with similar capacity. However, the L5 acquisition complexity with time domain correlation is 100 times more than L1 as its complexity increases with N2, meaning the cost and power to acquire L5 is out of reach. While using a time domain acquisition engine to acquire L5 may be possible for strong signals when the code and frequency search space is constrained for those signals, directly acquiring L5 with conventional methods would have serious shortcomings in many use cases.

Interestingly, the signal designers across all GNSS systems have cleverly designed the L5 signals so they can be easily acquired after acquiring their counterparts on L1. The L5 primary and secondary code is predictable based on learning the L1 primary code and navigation data bit phase. E5a and B2a primary and secondary codes can be predicted by learning the well-designed E1/B1 primary and secondary code phases that have the same total period: the combination of the 4 msec code lengths synchronous with 25 bits of secondary code are in phase with the E5a 100 msec overlay code. After an L1 fix with fine time, L5 can similarly be directly acquired easily with limited searching.

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Image: Xinhua News Agency

China has launched two satellites into medium-Earth orbits (MEO) for its BeiDou Navigation Satellite System, reported the China Satellite Navigation Office.

The satellites were carried by a Long March 3B rocket from the Xichang Satellite Launch Center in Sichuan province and are the 13th group of third-generation BeiDou satellites operating in MEO.

The two spacecraft will start formal operation after a period of in-orbit technical verification, according to the China Satellite Navigation Office.

BeiDou is China’s largest civilian satellite system and one of four global navigation networks, along with the United States GPS, Russia’s GLONASS and the European Union’s Galileo.

Since 2000, a total of 62 BeiDou satellites, including the first four experimental ones, have been lifted on 46 Long March 3 series rockets from Xichang.

In June 2020, the final satellite to complete Beidou’s third-generation network was lifted by a Long March 3B rocket launched from the Xichang center. The following month, the system was declared complete and began providing full-scale global services.

Nearly 50 Beidou satellites in active service, including the latest pair.

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Image: imaginima/iStock/Getty Images Plus/Getty Images

Fifty years since it was designed and approved by the U.S. Department of Defense (DOD), the GPS is at risk of losing its status as the world’s gold-standard location service, reported The Wall Street Journal.

In a recent paper published by Harvard’s Belfer Center for Science and International Affairs, “China’s BeiDou: New Dimensions of Great Power Competition,” Sarah Sewall, executive vice president for strategic issues at IQT and co-authors Tyler Vandenburg and Kaj Malden outline their finding that China’s version of GPS is part of the country’s longstanding effort to join the technological ranks of leading nations and use its capabilities to achieve geopolitical advantage across the globe.

Sewall’s assessment of BeiDou’s technical superiority received some unexpected support from a government advisory board on GPS, which stated that “GPS’s capabilities are now substantially inferior to those of China’s BeiDou,” and urged the administration to regain U.S. leadership in the field.

The BeiDou constellation is newer and has more satellites than any other system and has more than ten times as many monitoring stations around the world than GPS does. As a result, BeiDou’s accuracy is much better in many places, including the developing world.

Sewall points out that in cases where BeiDou provides the most accurate positioning, navigation, and timing (PNT) data, particularly in the global south, China may be able to influence other nations’ economies, stating that it is one example of “a new form of great power competition that most in the U.S. government don’t recognize.” China is providing superior PNT information to enhance its diplomatic, economic and military power and the United States cannot afford to cede this area of longstanding advantage.

BeiDou being newer and more advanced than other GNSS, makes it easier for China to encourage other nations to use its signals and purchase specialized equipment, especially when equipment purchases are heavily subsidized by the Chinese government, harming the U.S. economy and its status as the leader of GNSS technology.

Recent launch and surveillance fears

On May 16, 2023, China launched its most recent BeiDou satellite to replenish the constellation, bringing its total to 56 satellites, nearly twice as many as the 31 GPS satellites.

The latest BeiDou satellites also feature two-way messaging, a feature that GPS does not have. It is mainly available in China and requires special chips that are not widely available in the consumer market. It enables users to send short messages in areas without ground network cell coverage and can be used for search and rescue operations.

The CNBC report noted the fear that, with its most recent enhancements, the BeiDou system could be used as a surveillance device — as the two-way messaging feature reveals a user’s locations as well as other types of data.

Additionally, with the growing number of applications for cellphones and an increase in autonomous vehicles that use the BeiDou system, more and more user data is being transmitted.

The U.S. military is upgrading GPS with more-modern satellites that are designed to give nonmilitary devices more-precise coordinates in more indoor and hard-to-reach spaces. However, the next-generation GPS service for civilians is not expected to be released for several years.

GPS pioneered the PNT industry by offering civilians a new, free-to-use system. While originally developed for DOD, it turned into a critical global infrastructure that underlies a vast swath of the U.S. economy.

Besides GPS and BeiDou, there are two other global navigation satellite systems (GNSS), Russia’s Glonass and the European Union’s Galileo, as well as regional systems from Japan (QZSS) and India (NavIc).

BeiDou, once a small regional network with clunky receivers and few civilian users, has grown significantly since launching its first two satellites in 2000. It now has more than 30 precision-enhancing monitoring stations and claims to pinpoint users’ locations to within several centimeters, along with offering basic two-way communication capabilities.

Both BeiDou and GPS offer a variety of nonmilitary benefits that expand beyond the systems’ original expectations, from Uber drivers who often rely on a smartphones GNSS data to locate customers to farmers who can use GPS-based applications for farm planning, field mapping, solid sampling and more. GPS has been called “the silent utility” because signals are used in almost every technology, said Dana Goward, president of the Resilient Navigation and Timing Foundation.

Looking forward 

GPS guides U.S. missiles, ships and troops through more-secure military frequencies kept separate from its civilian signals. Its past dominance even made rival militaries reliant on the Pentagon-controlled system.

The U.S. military has long planned to upgrade GPS with a fleet of modernized and upgradable satellites that provide more-precise coordinates subject to less interference. The newer satellites broadcast data to civilian users over a new frequency called L5.

The Space Force has 17 L5-equipped satellites in orbit after a series of delays  but has yet to reach the 24 live satellites needed to run a reliable system. Some already-built satellites sit in a Colorado warehouse awaiting their turn for a funded launch.

The Space Force said in a statement that GPS continues to set the gold standard in its field.

“While other nations may report improvements in accuracy and equivalent performance in availability, GPS is still the clear leader in integrity and is the only system accepted for international flight use,” a spokeswoman for the branch’s Space Systems Command told The Wall Street Journal.

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The timing system is designed to support a vast array of scientific and technological applications as well as provide services when space-based signals are not available.

According to some Western observers, it is another example of China’s increasing lead over the United States in positioning, navigation, and timing (PNT) technology.

Its BeiDou satellite PNT system is newer and has been acknowledged superior in many ways to the U.S. Global Positioning System (GPS). This has allowed China to gain influence in some parts of the world at the expense of the United States.

Completion of the terrestrial system could have even more troubling implications for the United States.

Recent Announcements

On May 21 this year, a government affairs article in Shaanxi’s “The Paper” announced accelerated construction in Xi’an of a science center. Its centerpiece will be the country’s High-precision Ground-based Timing System. It is not entirely clear from the article whether this site will be the engineering and administrative headquarters for the system, or one of several “timing stations.”

The article also says the national system will be the largest in the world — with more than 20,000 kilometers of optical fiber and 295 time and frequency transmission sites — and will integrate space- and ground-based signals.

The network, according to the article, will supplement and improve the new eLoran (sometimes mistranslated by software as “Roland”) system in the western portion of the country. It will also support legacy eLoran “long-wave” signals in the east ensuring that the entire nation is well served.

Graphic from 2014 Chinese Academy of Sciences paper on Laron showing projected coverage in the western part of the country. Subsequent papers and announcements have indicated that western part of the network is complete or soon will be. (Image: Chinese Academy of Sciences)

Accuracy for the system’s fiber-optic transmissions is claimed to be less than 100 pico-seconds, with differential eLoran at less than 100 nanoseconds.

Experts in the West have confirmed that both these goals are achievable. Europe’s CERN laboratory has demonstrated picosecond level via fiber, and UK trials have shown the accuracy of differential eLoran to be within 50 nanoseconds.

Construction recently announced in Xi’an and Nagqu as part of China’s High-precision Ground-based Timing System.

A much shorter press release was issued on June 8, announcing groundbreaking for a “timing station” in Nagqu on the Tibetan plateau in China’s west. The announcement said that, once the station was complete, China will “…realize national soil coverage of long-wave [eLoran] timing signals…”

Expansion of its eLoran and fiber infrastructure to serve the entire nation gives China what some have called the “PNT resilience triad” — signals from space, from terrestrial broadcast, and over fiber. The three sources of delivery are sufficiently different that an accidental or malicious disruption of one is highly unlikely to impact the other ones. Users accessing all three should experience minimal to no impact.

Both the May and June announcements said that finishing the timing project will benefit China’s national economy and national security.

Timing is essential tech infrastructure. More precise and robust timing enables improvements to current applications and the creation of new ones. For example, better timing can enable greater spectrum efficiency with more throughput on existing frequency bands. Highly precise fiber-based timing could also support using 5G telecommunications networks for hyper-precise positioning in autonomy corridors serving self-driving vehicles, UAVs, and other systems.

China’s ground-based timing system is part of a larger plan by its National Timing Service Center for a system of systems approach to PNT. Described as a “comprehensive approach” at the Standford PNT Symposium in 2019, the architecture has satellite-based navigation at its heart and includes a wide variety of other capabilities.

Graphic showing China’s plan for multiple, mutually supporting, diverse methods of positioning, navigation, and timing service and data. (Presentation by China’s National Time Service Center at 2019 Standford PNT Symposium)

Some observers trace China’s national PNT efforts to an incident in 1996 during the Third Taiwan Strait Crisis. Chinese forces fired three missiles toward a point in the sea offshore of Tiawan’s Kee Lung naval base. Two of the missiles were lost. According to the People’s Liberation Army this was because the United States denied or altered GPS signals that the missiles were using for guidance.

Known by China’s military as “The Unforgettable Humiliation” the incident sparked decades of effort to ensure China would never again be dependent upon another nation or space for PNT. The BeiDou global navigation satellite system and the High-precision Gound-based Timing System are the two most noteworthy accomplishments in this regard.

Implications for the United States

China’s ever-increasing lead in essential PNT technology and infrastructure is of great concern to many in the United States.

China’s global navigation satellite system, Bei Dou, is newer and, according to a presidential advisory board, substantially superior to GPS in many ways. Using it as an instrument of “soft power,” China is offering other nations BeiDou signals, along with discounted user and support equipment, as part of its Belt and Road, and Digital Silk Road initiatives. Where successful, these efforts erode both GPS usage and U.S. influence.

Of greater concern to many are the “hard power” implications of China’s PNT dominance.

While China has and continues to develop multiple and resilient sources of PNT, in the United States “GPS is still a single point of failure,” according to a member of the National Security Council.

As a result, if China were to interfere with GPS in some way, a U.S. response in-kind against BeiDou would have much less impact. This strategic asymmetry has been described by former CIA senior analyst George Beebe as “an open invitation” for mischief or attack. One that could easily lead to an escalating series of responses ending in an armed conflict no one wants.

At a more tactical level, China’s eLoran system extends 1,000 miles offshore covering Taiwan, the Strait, and all approaches. In a conflict to capture the island and make it subject to the Communist regime, China could block all signals from space while preserving its forces’ ability to maneuver and communicate. Already at a disadvantage having to deploy far from their support bases, this would further hamper U.S., Japanese, and other forces hoping to help Taiwan maintain its independence.

The U.S. Department of Defense boasts it can operate well in GPS-denied environments and says it is also working on alternative means of navigation for deployed forces.

This begs the strategic question, though, of whether the United States would be willing to come to the aid of Taiwan or another ally if the homeland were threatened with a prolonged and crippling disruption of GPS services.

Prior to Russia’s invasion of Ukraine, the Kremlin destroyed a defunct satellite and boasted it would shoot down all 32 GPS satellites and “blind NATO” if the alliance intervened. Many observers have wondered whether that has played into subsequent U.S. and NATO policy toward the conflict.

Unfortunately, little has been done to eliminate the possibility of a belligerent adversary holding the U.S. homeland hostage through GPS.

For two decades narrow government and industry interests in GPS production have successfully opposed any effort they see as possibly “competing” for space in limited budgets. Appeals that such projects would increase system security by “taking the bullseye off” GPS satellites and signals have been to no avail.

However, this may be changing. Several years ago the National Guard began development of a national timing architecture and network, called NITRO. The project supports the Guard’s own requirements to be able to operate without GPS and to aid state first responders. It is already in use in 7 states.

The future of NITRO is unclear, though, as the Department of Defense sees it as a civil defense rather than a national defense project and is no longer supporting it in the budget. Yet, the National Guard’s funding flows through defense appropriations.

As of this writing, the National Guard and NITRO remain stuck in a bureaucratic and budgetary no-man’s land with no clear path forward.

<p>The post China finishing “High-precision Ground-based Timing System” – a worry for the United States first appeared on GPS World.</p>

In which markets and/or applications do you specialize?

We originally designed our LabSat simulator for ourselves, because we supply GPS equipment to the automotive market. Then, we decided to sell it into that market, which is our primary market, for other people to use. That’s where we started, but it has moved on since then. We supply many of the automotive companies who use it for testing their in-car GPS-based navigation systems.

However, we’ve moved on to our second biggest market, which is the companies that make deployment systems for internet satellites, which use it for end-of-life testing. Several of our customers use it. That’s because we do space simulations, so we can simulate the orbits of satellites. That’s very useful when they’re developing their satellites.

We supply many of the major GPS board manufacturers — such as NovAtel, Garmin, and Trimble — when they’re developing their boards and testing their devices. We supply many of the phone companies — such as Apple and Samsung — and many of the GPS chip manufacturers — such as Qualcomm, Broadcom, and Unicom. More or less any company that’s into GNSS.

How has the need for simulation changed in the past five years, with the completion of the BeiDou and Galileo GNSS constellations, the rise in jamming and spoofing threats, the sharp increase in corrections services, and the advent of new LEO-based PNT services?

It all started off very simple, with just GPS, which was one signal and one frequency. We got that up and working very well and it helped us a lot. Then we got into this market. In the last few years, we’ve had to suddenly invent 15 new signals. We do two systems, really: one is a record-and-replay system. You put a box in a car, on a bike, in a backpack, or on a rocket, and you record the raw GPS signals; then you can replay those on the bench. That requires greater bandwidth, greater bit depth, smaller size, battery power, all of that.

The other is pure signal simulation. We simulate the signals coming from the satellites from pure principles. So, we’ve had to dive into how those signals are structured, reproduce them mathematically, and then incorporate that in into our software. That’s been 15 times the original work we thought it would be, but as we add each signal it tends to get a bit simpler until they add new ways to encode signals, and then it gets complex again. We’ve had to increase our bandwidth, increase our bit depth for the recording to cover all of these new signals.
Because our systems record and replay, they’re used a lot to record real-world jamming. In many scenarios, our customers will take one of our boxes into the field and record either deliberate jamming or jamming that’s been carried out by a third party. Then they can replay that in the comfort of their lab.

With regards to spoofing, we’ve just improved our signal simulation. So, we can completely synchronize it with real time. We can do seamless takeover of a GNSS signal in real time. We can reproduce the current ephemeris and almanac. If we transmit a sufficiently powerful signal, we can completely take over that device. Then we can insert a new trajectory into it. That’s a very recent update we’ve done.

If the complexity and amount of your work has gone up so much in the last few years but you cannot increase your prices at the same rate, what does that do to your business model?

It’s the same people that produce the signals in the first place, so they still have a job. However, as we add more signals and capabilities, we tend to get more customers as well.

Oh, so, you’re expanding your market!

Right, right.

Regarding some of the new PNT services being developed, how do you simulate them realistically without the benefit of recordings of live sky signals?

It is all pure signals simulation. You go through the ICD line-by-line and work out the new schemes. Here’s an interesting anecdote. Our developer who does a lot of the signal development is Polish and is also fluent in Russian. When we were developing the GLONASS signals, he was working from the English version of the GLONASS ICD. He said that it didn’t make any sense. So, he looked at the Russian version and discovered that the English one had a typo. When he used the Russian version, everything worked perfectly. He told this to his contacts at GLONASS and they thanked him and updated the English translation of their document. So, you are very, very much reliant on every single word in that ICD.

Are there typically differences between the published ICD and the actual signal?

No, no. Apart from the Russian one, which had a typo, they’re very good. For example, we’ve recently implemented the latest GPS L1C signal. My developer spent six months recreating it and getting all the maths right and the only way you could test it was to connect it to a receiver and hit “go.” It just worked the first time. He almost fell off his chair. The ICD in that case was very, very accurate.

Hope that Xona’s ICD is just as good.

Yeah.

Are accuracy requirements for simulation increasing, to enable emerging applications?

Yes, absolutely. No one can have too much accuracy. Everyone’s chasing the goal of getting smaller, faster, and more accurate systems. They want greater precision and better accuracy from their simulators, as well as a faster response. We do real-time simulators and they want a smaller and smaller delay from when you input the trajectory to when you get the output. Luckily for us, Moore’s law is still in effect, so, as the complexity of the signals and the accuracy requirements increase, computers can churn through more data. Luckily, we’re able to keep up on the hardware side as well, because much of our processing is done using software. Some companies do it in hardware and some companies do it in software. We concentrate on the software side of things.

Here’s another interesting anecdote from my Polish guy. He noticed that the latest Intel chips contain an instruction that multiplies and divides at the same time but that it wasn’t available in Windows. So, he put in a request with Microsoft for that operational code and they incorporated it into the very latest version of dotnet, which has improved our simulation time by 7%. I see little improvements like that all the time.

Are all your simulators for use in the lab or are some for use in the field? If the latter, for what applications and how do they differ from the ones in the lab? (Well, for starters, I assume that they are smaller, lighter, and less power-hungry…)

All our systems are designed to be used inside and outside the lab. They can all be carried in a backpack, on a push bike, in a car. We do that deliberately, because we come from the automotive side of things, so we have to keep everything very small and compact.

Besides automotive, what are some field uses?

Some of our customers have put them in rockets, recording the signal as it goes up, or in boats. We have people walking around with an antenna on their wrist connected to one of our systems, so that they can simulate smartwatches. There are many portable applications. We have a very small battery-powered version, which makes it very independent.

Are there any recent success stories that you are at liberty to discuss?

Our most exciting one is a seamless transition for simulation that we developed to replace or augment GPS in tunnels. We’ve been talking to many cities around the world that are building new tunnels. Because modern cars automatically call emergency services when they crash or deploy their airbags, they need to know where they are, of course. Cities need to take this into account when they are building new tunnels, which can pass over each other or match the routes of surface streets. Therefore, accurate 3D positioning in the tunnels has become essential. It requires installing repeaters every 30 meters along each tunnel and software that runs on a server and seamlessly updates your position every 30 meters. As you enter a tunnel, your phone or car navigation system instantly switches to this system. It’s been received very well because it’s mainly software and the hardware is pretty simple. We’ve brought the cost down to a fifth of the cost of standard GPS simulators for tunnels. So, we’re talking to several cities about some very long tunnels, which is very exciting.

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What are currently the key challenges for simulation?

One of our big challenges is determining what performance requirements are necessary for our users. Often, they can’t determine what the specs need to be. All they know is that they need it to work. “I need this receiver from one company, this IMU from another company, and the simulator I got from you guys to work together and I need the performance to match reality.” It can be very challenging to say, “What are the requirements for the simulator? How accurate does it need to be? What types of things matter in this integration?”

Often, we’re left trying to figure that out. So, that’s an interesting, maybe unexpected challenge. It’s easy to look at the datasheet and see what some specs are, but it’s a much harder thing to say, “Well, what do you need the specs to be?” So, we’ve been working with our customers to try to nail down some of those specs, particularly with Wavefront. We have some specs on such things as phase alignment and phase stability. But how do you translate that into something like “Well, I just want the CRPA to work the same in the lab as it does in the real world?” There’s not a direct, easy way to do that. We’re in the middle of trying to figure that out. That’s definitely one of our challenges.

What about the increase in jamming and spoofing threats?

In the last five years, we’ve seen a lot more open talk about jamming and spoofing in the world. The receiver manufacturers must think about this a lot more. What’s interesting from a simulator point of view is that this is not actually new for us. We have the advantage that we’ve been designing to program requirements for years and they have included jamming and spoofing for years. So, in a way, simulation is ahead of this state of the world. Jamming and spoofing are not new or hard ideas for us. In fact, spoofing is similar to simulation. So, we already know how to do that.

Image: Safran Federal Systems (formerly Orolia Defense & Security)

However, jamming and spoofing are new to programs and integration labs. So, there might be platforms where they’re now testing against jamming or spoofing requirements where in the past, maybe they didn’t do that. They certainly can use our simulators to help them do that. However, we’re not seeing a lot of new requirements coming to us saying we need new jamming or spoofing capabilities, because we already have them. Luckily, we are future oriented regarding the jamming and spoofing requirements, so those really haven’t been a challenge for us yet.

That can always change, right? If new requirements come up, such as higher data rates or wider bandwidth waveforms or different types of waveforms, then we would have to adapt and add support for that kind of stuff. As of right now, however, we aren’t really seeing that. So, luckily, we’re prepared for that. As for the industry as a whole, there has definitely been a big movement in the last few years to understand the effects of jamming and spoofing. Simulation is a big part of that.

What about the completion of the BeiDou and Galileo constellations?

For a long time, we simulated four constellations. Then that began to get fuzzy. Do you consider SBAS a constellation or is that just an augmentation? Do you count EGNOS and other supplemental constellations for the other constellations? What about NavIC and QZSS? Before you know it, you start to lose track of exactly how many you have. We just released our 8th constellation, Xona.We’re going to be demonstrating it at JNC.

Tell me more about that.

We are trying to have all the constellations and that can be a fuzzy definition. Does that mean all that are up there right now or all that will be up there in the future? We’re trying to be forward looking and add everything that is going to be up there or might be up there so that lab users can develop and test. Multi-constellation simulation is a particularly challenging problem for groups that don’t have simulators. If you’re just doing research on, say, GPS, and want a new code, you might be able to do that in a lab on your own. But as soon as you say, “I want to do research on whether this LEO constellation helps navigation on a receiver that also uses Galileo and GPS,” suddenly, your research requires a full multi-constellation simulation.
There are two choices. One is to have a simulator do the constellations that already exist, and then you have some research to add constellations. That can be very challenging, especially with time alignment and things like that. The other is to have a simulator that can do all the constellations. That would be the easy choice, right? That presents a problem with such things as LEO navigation being on the rise and these constellations that are just emerging, that are still not even fully defined.

So, we’re trying to build those into our simulation products, to help researchers and decision makers determine whether these will be useful features to add to their receivers or their systems. We have the advantage of having a software-defined architecture. We designed the software so that it is easy to add new constellations to it. Basically, once we’re given a proper ICD, we’re only a couple of months away from a first draft implementation of that new signal. Then we iterate. There used to always be a government-driven, multi-year program to develop an ICD. Now, we have this new concept of the signal manufacturers. We’re seeing private companies release signal specs. That’s a very different way of creating a signal in a constellation. So, sometimes you don’t get much time between when the ICD is available and when simulator users want to use that constellation. Having a software-defined architecture really helps us move quickly. We can add such things as Xona very fast.

Xona told me a couple of days ago that they will soon put out an ICD. What’s the difference between actual signals that you can record and play versus something that’s only on paper?

That’s a great point. Probably many people don’t realize, when they first look at this, that what’s in the ICD and what’s on live sky are sometimes very different. Is the simulator supposed to match live sky? Or is it supposed to match the intended final state of the constellation, according to the ICD? This is a huge topic for M-Code, which is ever changing, and has a very large ICD that’s been released. Space Systems Command/Military Communications & Position, Navigation, & Timing (MCPNT) controls the features and releases them incrementally. We’re constantly having to make changes to the simulator to match those releases. The same is true for the other ICDs. At the Institute of Navigation Joint Navigation Conference (JNC), we will demonstrate an expanded PRN. I think this showed up in the ICD a couple of years ago, but it’s not used by any users yet. Some of the receiver manufacturers are starting to look at using PRNs beyond 32. So, we’re adding that to the simulator. This has already happened for BeiDou as well. I think their ICD goes up to more than 60 satellites. It’s an ever-changing race. The ICDs are constantly being updated and we’re trying to update the simulator.

Image: Safran Federal Systems (formerly Orolia Defense & Security)

Meanwhile, live sky is many years behind the paper, right? This creates an interesting challenge: when you design a system, are you designing it for today or for the future? We have users in both groups. We have users that only care about what is happening today, because they need a model. Maybe you want to model a specific mission and you want to make sure that everything’s going to go properly. Or maybe you’re designing a system that you want to release in three or four years, and you want to make sure that it’s going to work with the state of the system then.

A big challenge is to make sure that we’re keeping pace with all these ICDs. There are more constellations than ever and the technology makes it easier to change signal architectures. We’re seeing signals change faster than we’ve ever seen them change before. We go to conferences and hear about such things as on-orbit reprogramming and signals that might even change specs while they’re being transmitted. Maybe they don’t even have to have a fixed bandwidth or fixed bit rate. We’re going to start talking about signals that can reprogram on the fly. That’s going to make simulation more and more challenging. The technology exists to do this.

Software-defined waveforms is a very logical step. In the software world, we have this concept of version nightmare. When you have 20 different pieces of software that are interdependent, it can get very challenging. We’re going to start to see that in simulators. We’re going to see, “Hey, what version of navigation authentication are you using? We updated it six months ago. Are you using the new one or the old one? Which one should we use?” Well, it depends on what your receiver is using. It’s going to be interesting and challenging to keep all this straight in the next few years as things evolve. Certainly, however, our goal is to be there for all of it and to be as fast and as forward thinking as we can for our customers. That means that we also need to know what our customers need. So, we’re always looking for feedback and requests, what challenges our customers face and we’re responding to those requests.

Tell me more about the difference between simulators used by receiver manufacturers in their labs as they’re tweaking receivers or developing new ones vs. simulators used for mission planning.

The simulators are the same, but they’re used in very different ways. In most lab simulation, what the constellation looks like that day doesn’t matter very much. They can just run with a default constellation for a given day. They’ll run that scenario hundreds or thousands of times and never need to change it because they’re testing parts of the receiver that don’t care a whole lot about the specifics of what’s happening.

Whereas missions are time- and location-specific.

Yeah, exactly. They want to know which satellites will be overhead at an exact time and place. It’s not so much a problem anymore, but there used to be certain days and times when you would not get enough satellites in view, or you might have very bad dilution of precision, and your mission might actually fail. We’re past those days. There are now enough satellites up there. Most receivers will navigate within their specs most of the time in most places. However, for critical missions, such as military operations or rocket launches, you might not want to just assume that any day is a good day. So, if you’re about to launch a rocket, you might want to check. “What does the constellation look like right now?” The challenges that brings is that simulators have a default constellation, but the constellations are constantly changing.

When you’re doing real day mission planning, the big problem isn’t so much how to generate a signal, it’s how to find out what’s happening today. That’s really the nature of the problem because what’s out there today is different from what was happening yesterday or what will happen tomorrow. You might have unhealthy satellites. You need to know that if you want to model them. It becomes a big challenge to get all the right data into the simulator. Once all that data is in there, then it’s the same as any other simulation.

Are there good sources for current data on GLONASS, BeiDou and Galileo?

Image: Safran Federal Systems (formerly Orolia Defense & Security)

There are a couple of websites that provide information about where the satellites currently are. However, we’ve found that each one of those sites has its own challenges. Some are maybe 30 minutes out of date, which is pretty good, but puts the satellites in slightly different spots. Some of those sites only support some of the constellations. We’re talking about multiple countries and they don’t all agree on how this should be done. So, there’s not a single point that you can visit to get all the satellite data. There are a couple of companies that try to fix this. U-blox has AssistNow; Qualcomm has an assist for its cellular receivers; Trimble, NovAtel, and a couple of other companies have their error correction services to which you can subscribe to get some of that data.

If you want real-time up-to-date ephemeris for all the constellations, that is challenging. There are one or two options we have found that seem to work, but they each have their disadvantages. Maybe they don’t have all the satellites. Again, we’re talking about versioning issues. So, if you’ve designed your system with a certain version of an ICD and they’ve added more satellites since, those new SVs maybe aren’t so important for your users, so you don’t publish them. Other users want those satellites. So, we see versioning issues in these data streams. For example, we use the CORS network to get a lot of GPS data but that whole network, as far as I know, is only running the legacy data. As far as I know, no network is distributing the L1C modernized data that we will need at some point. So, as we launch new signals and constellations, we need the networks to provide this new data.

What are some other challenges?

For us, being a software-defined simulator on a platform dependent on software-defined radio (SDR), we’re constantly looking at what’s changing in the SDR technology community. There’s always some interesting stuff happening there that we try to incorporate. We don’t have any big announcements this year, as far as new architectures or anything like that. However, the SDR community is evolving. It’s still a rather new industry. A few years ago, we were an early adopter of SDR technology for mass deployment. Now, we’re seeing some more mature SDRs starting to push such things as channel count and coherency. We will probably take advantage of that in the future.

The other interesting thing technology-wise is that we’re also a GPU-dependent technology. So, as the GPU industry continues to evolve and makes bigger and faster GPUs, we get a relatively low-cost way to upgrade. We don’t have to do a lot of R&D to upgrade to a new GPU. For our users that means that the number of signals they can generate on their simulators is always increasing even using the same hardware from one generation to the next. Our first simulator did 75 signals; the next version did 150. We could build a system that did more than 1,000 signals, but our users don’t need it.

I assume that the growth curve for GPUs is steeper than that for signals.

I think that you’re right about that. I’m sure glad they do, because then something like Xona shows up and we don’t have to rearchitect our system to generate 300 signals, right? At JNC we will show expanded PRN, 300 Xona satellites in the constellation, and a 10 fold improvement on Wavefront performance specs.. We will continue to build simulators that meet our customers’ requirements. Besides GPUs, a lot of the technology involves software R&D and signals. The stuff that we do digitally inside of our system that allows us to do things like extremely precise phase alignment on Wavefront, for example. We spent a lot of time developing that stuff.

<p>The post Faux signals for real results: Safran Federal Systems first appeared on GPS World.</p>

Figure 1: Diagram of cis-lunar space, which includes the real GPS sidelobe data collected on an HEO space vehicle. (All figures provided by the authors)

As part of NASA’s increased interest in returning to the moon, the ability to acquire accurate, onboard navigation solutions will be indispensable for autonomous operations in cis-lunar space (see Figure 1). Artemis I recently made its weeks-long journey to the Moon, and spacecraft carrying components of the Lunar Gateway and Human Landing System are planned to follow suit. During launch and within the GNSS space service volume, space vehicles can depend on the robust navigation signals transmitted by GNSS constellations (GPS, GLONASS, BeiDou, and Galileo). However, beyond this region, NASA’s Deep Space Network (DSN) serves as the system to track and guide lunar spacecraft through the dark regions of cis-lunar space. Increasingly, development of a lunar navigation satellite system (LNSS) that relies on a low size, weight and power (SWaP) “smallSat” constellation is being discussed for various possible orbits such as low lunar orbit (LLO), near rectilinear halo orbit (NRHO) and elliptical frozen orbit (ELFO).

Figure 2: DPE 3D (left) and 2D (right) spatial correlogram shown on a 3D north-east grid.

We have implemented direct positioning estimation (or collective detection) techniques to make the most of the limited and weak GPS signals (see Figure 2) that have been employed in other GNSS-degraded environments such as urban canyons. The algorithm used in conventional GNSS positioning employs a two-step method. In the first step, the receiver acquires signals to get a coarse estimate of the received signal’s phase offset. In the second step, the receiver tracks the signals using a delay lock loop coupled with a phase or frequency lock loop. The second step enables the receiver to get fine measurements, ultimately used to obtain a navigation solution. In the scenario addressed in our work, where a vehicle is navigating beyond the GPS satellite constellation, the signals are weak and sparse, and a conventional GPS receiver may not be able to acquire or maintain a lock on a satellite’s sidelobe signals to form a position solution. For a well-parameterized region of interest (that is, having a priori knowledge of the vehicle orbital state through dynamic filtering), and if the user’s clock error is known within a microsecond, a direct positioning estimator (DPE) can be used to improve acquisition sensitivity and obtain better position solutions. DPE works by incorporating code/carrier tracking loops and navigation solutions into a single step. It uses a priori information about the GPS satellites, user location, and clocks to directly estimate a position solution from the received signal. The delay-Doppler correlograms are first computed individually for the satellites and are then mapped onto a grid of possible candidate locations to produce a multi-dimensional spatial correlogram. By combining all signals using a cost function to determine the spatial location with the most correlation between satellites, the user position can be determined. As mentioned, signals received beyond the constellation will be sparse and weak, which makes DPE a desirable positioning method.

BACKGROUND

The proposed techniques draw from several studies exploring the use of weak signals and provide a groundwork for developing robust direct positioning methods for navigating beyond the constellation. NASA has supported and conducted several of the studies in developing further research into the use of signals in this space.

A study done by Kar-Ming Cheung and his colleagues at the Jet Propulsion Laboratory propagates the orbits of satellites in GPS, Galileo, and GLONASS constellations, and simulates the “weak GPS” real-time positioning and timing performances at lunar distance. The authors simulated an NRHO lunar vehicle based on the assumption that the lunar vehicle is in view of a GNSS satellite as long as it falls within the 40-degree beamwidth of the satellite’s antenna. The authors also simulate the 3D positioning performance as a function of the satellites’ ephemeris and pseudorange errors. Preliminary results showed that the lunar vehicle can see five to 13 satellites and achieve a 3D positioning error (one-sigma) of 200 to 300 meters based on reasonable ephemeris and pseudorange error assumptions. The authors also considered using relative positioning to mitigate the GNSS satellites’ ephemeris biases. Our work differs from this study in several key ways, including using real data collected beyond the GNSS constellations and investigating the method of direct positioning estimation for sparse signals.

Luke Winternitz and colleagues at the Goddard Space Flight Center described and predicted the performance of a conceptual autonomous GPS-based navigation system for NASA’s planned Lunar Gateway. The system was based on the flight-proven Magnetospheric Multiscale (MMS) GPS navigation system augmented with an Earth-pointed high-gain antenna, and optionally, an atomic clock. The authors used high-fidelity simulations calibrated against MMS flight data, making use of GPS transmitter patterns from the GPS Antenna Characterization Experiment project to predict the system’s performance in the Gateway NRHO. The results indicated that GPS can provide an autonomous, real-time navigation capability with comparable, or superior, performance to a ground-based DSN approach using eight hours of tracking data per day.

In direct positioning or collective detection research, Penina Axelrad and her colleagues at the University of Colorado at Boulder and the Charles Stark Draper Laboratory explored the use of GPS for autonomous orbit determination in geostationary orbit (GEO). They developed a novel approach for directly detecting and estimating the position of a GEO satellite using a very short duration GPS observation period that had been presented and demonstrated using a hardware simulator, radio-frequency sampling receiver, and MATLAB processing.

Ultimately, these studies and more have directed our research in exploring novel methods for navigating beyond the constellation space.

DATA COLLECTION

The data we used was collected as part of the U.S. Air Force Academy-sponsored Falcon Gold experiment and the data was also post-processed by analysts from the Aerospace Corporation. A few of the key notions behind the design of the experiment was to place an emphasis on off-the-shelf hardware components. The antenna used on board the spacecraft was a 2-inch patch antenna and the power source was a group of 30 NiMH batteries. To save power, the spacecraft collected 40-millisecond snapshots of data and only took data every five minutes. The GPS L1 frequency was down-converted to a 308.88 kHz intermediate frequency and was sampled at a low rate of 2 MHz (below the Nyquist rate) and the samples were only 1- bit wide. Again, the processing was designed to minimize power requirements.

METHODS AND SIMULATIONS

To test our techniques, we used real data collected from the Falcon Gold experiment on a launch vehicle upper stage (we’ll call it the Falcon Gold satellite) which collected data above the constellation on a HEO orbit. The data collected was sparse, and the signals were weak. However, the correlation process has shown that the collected data contained satellite pseudorandom noise codes (PRNs). Through preliminary investigation, we find that the acquired Doppler frequency offset matches the predicted orbit of the satellite when propagated forward from an initial state. The predicted orbit of the satellite was derived from the orbital parameters estimated using a batch least-squares fit of range-rate measurements using Aerospace Corporation’s TRACE orbit-determination software. The propagation method uses a Dormand-Prince eighth-order integration method with a 70-degree, first-order spherical harmonic gravity model and accounting for the gravitation of the Moon and Sun. The specifics of this investigation are detailed below.

Figure 3: GPS constellation “birdcage” (grey tracks), with regions of visibility near the GPS antenna boresight in blue and green for the given line-of-sight from the Falcon Gold satellite along its orbit (orange).

The positions of the GPS satellites are calculated using broadcast messages (combined into so-called BRDC files) and International GNSS Service (IGS) precise orbit data products (SP3 files). GPS satellites broadcast signals containing their orbit details and timing information with respect to an atomic clock. Legacy GPS signals broadcast messages contain 15 ephemeris parameters, with new parameters provided every two hours. The IGS supports a global network of more than 500 ground stations, whose data is used to precisely determine the orbit (position and velocity in an Earth-based coordinate system) and clock corrections for each GNSS satellite. These satellite positions, along with the one calculated for the Falcon Gold satellite, allowed for the simulation of visibility conditions. In other words, by determining points along the Falcon Gold satellite trajectory, we determine whether the vehicle will be within the 50° beamwidth of a GPS satellite that is not blocked by Earth.

Figure 3 shows a plot rendering of the visibility conditions of the Falcon Gold satellite at a location along its orbit to the GPS satellite tracks. Figure 4 depicts three of the 12 segments where signals were detected and compares the predicted visibility to the satellites that were actually detected. A GPS satellite is predicted to be visible to the Falcon Gold satellite if the direct line-of-sight (DLOS) is not occluded by Earth and if the DLOS is within 25° of the GPS antenna boresight (see Figure 5).

Figure 4: Predicted visibility of direct line-of-sight to each GPS satellite where a blue line indicates the PRN is predicted to be visible but undetected. A green line is predicted to be visible and was detected, and a red line indicates that the satellite is predicted to not be visible, but was still detected.

Figure 5: Depiction of the regions of a GPS orbit where the Falcon Gold satellite could potentially detect GPS signals based on visibility.

As a preliminary step to evaluate the Falcon Gold data, we analyzed the Doppler shifts that were detected at 12 locations along the Falcon Gold trajectory above the constellation. By comparing the Doppler frequency shifts detected to the ones predicted by calculating the rate of change of the range between the GPS satellites and modeled Falcon Gold satellite, we calculated the range rate root-mean-square error (RMSE). Through this analysis, we were able to verify the locations on the predicted trajectory that closely matched the detected Doppler shifts.

These results are used to direct our investigations to regions of the dataset to parameterize our orbit track in a way to effectively search our delay and Doppler correlograms to populate our spatial correlograms within the DPE. Figure 6 shows the time history of the difference of predicted range rates on the trajectory and the detected range rates on the trajectory. That is, a constant detected range rate value is subtracted from a changing range rate for the duration of the trajectory and not just at the location on the trajectory at the detect time (dashed vertical line). From this we can see that the TRACE method gives range rates near the detected ranges at the approximate detection time for the 12 different segments.

Figure 6: Plots depicting the 12 segments of detection and the corresponding time history of differences of range-rate values for each GPS PRN detected. The time history is of the range-rate difference between the predicted range rate from the TRACE-estimated trajectory and the constant detected range rate at the detection time (vertical line).

Excluding Segment 12, which was below the MEO constellation altitude, Segment 6 has more detected range rates than that of the other segments. On closer inspection of this segment, and using IGS precise orbit data products, it appears that the minimum RMSE of the range rates from the detected PRNs is off from the reported detection time by several seconds (see Figure 7). Investigating regions along the Falcon Gold TRACE-estimated trajectory and assuming a mismatch in time tagging results in a location (in Earth-centered Earth-fixed coordinates) with a lower RMSE for the predicted range rates compared to detected range rates.

Figure 7: Range-rate difference between the predicted range rate from the TRACE-estimated trajectory and the constant detected range rate at the detection time (left). A portion of the trajectory around Segment 6 with the TRACE-estimated location at the time of detection (red) and the location with the minimum RMSE of range rate (black).

To determine the search space for the DPE, we first determine the location along the original TRACE-estimated trajectory with the minimum RMSE of range rates for each segment. Then we propagate the state (position and velocity) at the minimum location to the Segment 6 time stamp. If the time segment has more than three observed range rates (Segment 6 and Segment 12), we perform a least squares velocity estimate using the range-rate measurements, using the locations along the trajectory and selecting the location with the smallest RMSE. Then, for Segment 12, the position and velocity obtained from least squares is propagated backwards in time to the Segment 6 timestamp. All of these points along the trajectory as well as the original point from the TRACE estimated trajectory are used in a way similar to the method of using a sigma point filter. Specifically, the mean and covariance of the position and velocity values are used to sample a Gaussian distribution. This distribution will serve as the first iteration of the candidate locations for DPE. There were a total of three iteration steps and at each iteration the range of clock bias values over which to search was refined from a spacing of 1,000 meters, 100 meters, and 10 meters. Also on the third iteration, the sampled Gaussian distribution was resampled with 1,000 times the covariance matrix values in the directions perpendicular to the direction to Earth. This was done to gain better insight into the GPS satellites that were contributing to the DPE solution.

RESULTS

Figure 8 shows the correlation peaks for each of the signals reported to be detected using a 15-millisecond non-coherent integration time within the DPE acquisition. Satellite PRNs 4, 16 and 19 are clearly detected. Satellite PRN 29 is less obviously detected, but the maximum correlation value is associated with the reported detected frequency. However, this is the peak detected frequency only if the Doppler search band is narrowly selected around the reported detected frequency. Similarly, while the peak code delay shows a clear acquisition peak for PRNs 4, 16 and 19, for PRN 29 the peak value for code delay is more ambiguous with many peaks of similar magnitude of correlation power. Figure 8 depicts the regions around the max peak correlation chip delay.

Figure 8: Acquisition peak in frequency (left) and time (right) for PRN 4, 16, 19 and 29. The correlograms are centered on the frequency predicted from the range rate calculated along the trajectory.

For the first iteration of DPE, the peak coordinated acquisition values for PRN 16 and PRN 4 are chosen for the solution space. From the corresponding spatial correlogram, the chosen candidate solution is roughly 44 kilometers away from the original position estimated using TRACE.
For the second iteration of DPE, the clock bias is refined to search over a 100-meter spacing. The peak values for PRN 16 and PRN 19 are chosen for the solution space and the chosen candidate solution is roughly 38 kilometers away from the original position estimated using TRACE.
For the final iteration, Figures 9 and 10 depict the solutions with the 10-meter clock bias spacing and the approach of spreading the search space over the dimension perpendicular to the direction of Earth. Again, this was done to illustrate how the peak correlations appear to be drawing close to a single intersection location. However, the results fall short of the type of results shown in the spatial correlogram previously depicted in Figure 2 when many satellite signals were detected.

Figure 9: Acquisition peaks plotted in the time domain with the candidate location chosen at the location of the vertical black line for the detected PRNs for the third iteration of the DPE method.

Figure 10: Spatial correlogram with the candidate location chosen at the location of the black circle for the detected PRNs for the third iteration of DPE method. The original TRACE-estimated position is indicated by a red circle. The two positions are approximately 28 kilometers apart.

A similar iterative method was followed using not just the four detected PRNs, but any satellite that was predicted to be visible with the relaxed criteria allowing for visibility based on receiving signals from the first and second sidelobes of the antenna. This is predicted using a larger 40° away from the GPS antenna boresight criterion. The final spatial correlogram (Figure 11) shows similar results to the intersections shown in Figure 10. However, there is potentially another PRN shown with a peak contribution near the original intersection point. These results are somewhat inconclusive and will need to be investigated further.

Figure 11: Spatial correlogram with the candidate location chosen at the location of the black circle for the detected PRNs for the third iteration of DPE method using additional satellites. The original TRACE-estimated position is indicated by a red circle. The two positions are approximately 24 kilometers apart.

CONCLUSIONS AND FUTURE WORK

Our research investigated the DPE approach of positioning beyond the GNSS constellations using real data. We will further investigate ways to parameterize our estimated orbit for use within a DPE algorithm in conjunction with other orbit determination techniques (such as filtering) as our results were promising but inconclusive. Some additional methods that may aid in this research include investigating the use of precise SP3 orbit files over the navigation message currently used (BRDC) within our DPE approach. Also, some additional work will need to be completed in determining the possibility of time tagging issues that could result in discrepancies and formulating additional methods related to visibility prediction that could aid in partitioning the search space. Additionally, we plan to investigate other segments where few signals were detected, but where more satellites are predicted to be visible (a better test of DPE). Finally, using full 40-millisecond data segments rather than the 15 milliseconds used to date may provide the additional signal strength needed to give more conclusive results.

ACKNOWLEDGMENTS

This article is based on the paper “Direct Positioning Estimation Beyond the Constellation Using Falcon Gold Data Collected on Highly Elliptical Orbit” presented at ION ITM 2023, the 2023 International Technical Meeting of the Institute of Navigation, Long Beach, California, January 23–26, 2023.

KIRSTEN STRANDJORD is an assistant professor in the Aerospace Engineering Department at the University of Minnesota. She received her Ph.D. in aerospace engineering sciences from the University of Colorado Boulder.

FAITH CORNISH is a graduate student in the Aerospace Engineering Department at the University of Minnesota.

<p>The post Far Out: Positioning above the GPS constellation first appeared on GPS World.</p>

Image: BeiDou program

More than that, it is one example of “a new form of great power competition that most in the U.S. government don’t recognize,” she said. China is providing superior precision, navigation, and timing information to enhance its diplomatic, economic and military power and the United States cannot afford to cede this area of longstanding advantage.

In a recent paper published by Harvard’s Belfer Center for Science and International Affairs, “China’s BeiDou: New Dimensions of Great Power Competition,” Sewall and co-authors Tyler Vandenburg and Kaj Malden outline their finding that China’s version of GPS is part of a longstanding effort to join the technological ranks of leading nations and leverage its capabilities to achieve geopolitical advantage in many areas.

“First, the global reach of BeiDou ensures that the Peoples’ Liberation Army is no longer dependent on another nation’s satnav. China’s economy — and those of other nations relying on BeiDou — can continue to function even if GPS is degraded or denied,” Sewall stated. “This may increase Beijing’s incentives to attack other national satellite capabilities.”

“BeiDou is also an economic driver for the Chinese economy and innovation. The output of China’s commercial space and navigation services industry has increased by tens of billions in the last decade, and new applications such as precision agriculture and self-driving cars show no sign of slowing,” Sewall continued.

The focus of Sewall’s paper, though, is the way BeiDou supports China’s Belt and Road and Digital Silk Road initiatives to gain influence and leverage around the world. She points out that in cases where BeiDou provides the most accurate positioning, navigation, and timing (PNT) data, particularly in the global south, China may be able to hold much of another nation’s economy hostage.

The BeiDou constellation has more satellites than GPS or any other system. It also has more than ten times the monitoring stations in other countries than have been deployed for GPS. As a result, in many places, particularly in the developing world, BeiDou’s accuracy is much better.

Her assessment of BeiDou’s technical superiority received some unexpected support recently from a government advisory board on GPS. It reported that “GPS’s capabilities are now substantially inferior to those of China’s BeiDou,” and urged the administration to regain U.S. leadership in the field.

Being newer and more advanced makes it easier for China to encourage other nations to use BeiDou signals and purchase specialized equipment, especially when equipment purchases are heavily subsidized by the Chinese government.

This is important because systems such as GPS and BeiDou provide more than just directions to the nearest coffee shop. Their precise PNT signals are used for everything from synchronizing cellphone networks and industrial machine controls, to time stamping financial transactions, and coordinating electrical grids. GPS has been called “the silent utility” because signals are used in almost every technology.

“It is very difficult for government leaders in the developing world to turn down discounted infrastructure and opportunities for economic development,” Sewall said. “Even if they know that tying that infrastructure to Chinese signals may give the CCP [Chinese Communist Party] a future on/off switch to their economies.”

The West and the United States in particular, faces challenges confronting China’s efforts with BeiDou, according to Sewall.

“Many in government equate national power with military power, but that’s a narrow and insufficient formulation, particularly in the 21^st century,” Sewall said. “American officials under appreciate China’s efforts to create commercial technology dependencies abroad. The United States has left a vacuum in the developing world that our industry is seemingly unable to fill in the face of competition from Chinese firms that are heavily supported by their government.”

Sewall describes a Chinese “tech stack” being exported that include BeiDou services as part of Belt and Road and Digital Silk Road. It is comprised of a hierarchy of equipment that includes network cables, servers, and cell phones.

“We don’t really have a democratic approach to help foreign nations make meaningful technology choices. We risk ceding global infrastructure to China if we fail to help Western firms offer their own integrated products and services to the developing world,” she said.

If we recognized this new form of great power competition, America could easily leap frog China in areas such as satellite navigation, said Patrick Diamond, a member of the President’s Advisory Board on GPS.

“We could provide higher accuracy GPS and make signals much more secure though internet delivered authentication,” Diamond said. “We could offer complementary terrestrial systems to GPS that would give other nations their own sovereign source of precise time and location while at the same time cooperating with our signals from space.”

“Competing effectively with China in the coming decades will require Americans to think more holistically,” Sewall said, “from realizing that GPS is not just about the military and space, to understanding that national power is more than the ability to prosecute war.”

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