IT’S ALL PHYSICS. How things work, that is. Well, maybe a little chemistry too in some cases. I might be a little biased in my opinion given that I’m an applied physicist by training. Radio? Satellite navigation? Yes, the principles of their operation are all governed by physics. Many physicists of my generation started out as radio tinkerers. I’ve recounted in this column before that I built my first radio (from a kit) when I was 14 (not counting the crystal radio that my father helped me to put together when I was 8 or 9). I built a few more during high school, got into radio astronomy as an undergraduate and did a Ph.D. in the application of very long baseline (radio) interferometry to geodesy.
The great American physicist Richard Feynman was also a radio tinkerer in his youth. He recounts in one of his autobiographical books how he used to fix radios. Since he would approach the task of repairing each non-functioning set by first contemplating why it wasn’t working, he got the reputation of fixing radios by thinking!
One of Feynman’s special abilities was in explaining how things worked. In fact, he has been called “The Great Explainer.” He authored what is arguably the best physics textbooks ever produced: The Feynman Lectures on Physics. The three-volume set, developed from his Caltech lectures to undergraduates between 1961 and 1964, covers mechanics, radiation, electromagnetism, matter and quantum mechanics. Many students and practicing physicists have learned or relearned aspects of physics from the famous “red books.” Many more will now thank Caltech, which recently put the Lectures online for anyone to read.
In the February 2016 column, we learned about the development of a microprocessor-controlled multi-element GNSS antenna array for interference rejection. While there are many textbooks that describe how multi-element antennas work, Feynman explains their operation in his Lectures from first principles–from the principles of physics. The phenomenon governing the behavior of antennas with multiple elements is called interference.
If we combine two electromagnetic waves, they will interfere with each other with a result that depends on the relative phase (or phase difference) of the waves. The waves might reinforce each other leading to a larger net amplitude, called constructive interference, or partially or fully null each other out, called destructive interference. When we apply this concept to the signals transmitted by a pair of antennas making up an array in a horizontal plane, we find that the array has directionality. That is, if we space the antennas by one-half wavelength of the signal to be transmitted and feed the antennas in phase (zero phase difference), we will transmit a strong signal in the directions perpendicular to the baseline connecting the antennas (say east-west) and no signal in the orthogonal directions (north-south). If we use this antenna pair for receiving, we will have a null in the reception pattern to the north and to the south and will be insensitive to signals arriving from those directions. And as Feynman describes in his lectures, by adding more antennas to the array and “some cleverness in spacing and phasing our antennas,” we can have a fairly narrow pattern null in a chosen direction. In the case of a GNSS antenna array, that direction might be that of a jamming signal and so we can null out the jammer and maintain a positioning capability.
There is more to it in developing a practical microprocessor-controlled GNSS antenna array, but it starts with the physics.
This is my 300th and last “Innovation” column in GPS World. I have mixed feelings about stopping the column. I’ve really enjoyed doing it for the past 35 years, but editorial deadlines can be difficult to meet sometimes, especially when I’ve got other things to get done or if they come in the middle of a vacation.
To rephrase the old adage, editorial deadlines wait for no one. Looking back, I don’t know how I managed to initially produce six and then 10 columns each year, along with all my other duties as a university professor. Mind you, as I’ll soon discuss, most of the articles in the columns were authored by others. My job mostly was to edit the articles to help the authors tell their stories in a particular GPS World style and sometimes to improve their submitted figures. Additionally, in 2006, I started to write a sidebar called “Insights” to provide some basic background material about each column’s topic. A few years ago, I became editor-in-chief of the Institute of Navigation’s journal NAVIGATION, which takes up a bit of my time, along with lecturing and managing a research team. So, at 75, I thought it might be a good time to lessen the load a little bit.
In this last column, I’m going to tell the story of how “Innovation” came to be and review some of the column’s developments over the years.
How it all began
In the fall of 1989, GPS World’s founding editor, Glen Gibbons, approached Dave Wells, Ph.D., a fellow faculty member in the then Department of Surveying Engineering at the University of New Brunswick (UNB) – about assisting with a “technology/product development column” in the magazine he was about to start. Glen wanted it to provide “an analysis and commentary on the research, development, product issues and needs of the GPS community.” And, since GPS World readers would have marked differences in their knowledge and expertise in the GPS area, “the column should deal with issues that have broad application and interest and are presented in terms that are accessible to as wide a range of readers as possible,” Glen said in a letter to Dave.
Glen had heard about Dave’s (and UNB’s) early involvement with GPS. When I came to UNB in 1981, UNB was already carrying out some of the first theoretical studies on how GPS could be used by surveyors and geodesists for precise positioning. Shortly afterwards, UNB participated in some of the first surveys using the Macrometer V-1000 and Texas Instruments TI 4100 receivers and developed software to process the resulting data. In 1983, Dr. Gerhard Beutler from the Astronomical Institute of the University of Bern came to UNB on a sabbatical and began developing his own GPS data processing software that would eventually become the Bernese GNSS Software or just “Bernese” to those in the know. Somehow, in between our GPS algorithm and software development, teaching, mentoring graduate students and other duties, we managed to self-publish the first textbook on GPS, Guide to GPS Positioning. With a publication date of December 31, 1986, it went on to sell more than 12,000 copies in the English version alone. It was also translated into Chinese, Spanish and Vietnamese. So, perhaps it is not surprising that Glen came to knock on UNB’s door when he was starting up his magazine.
Getting back to Glen’s letter, he went on to say, “It would be possible to handle the preparation or presentation of the column in one of several ways: We could identify a single person who would have primary responsibility for writing all the columns and whose byline would appear on them; we could have a person act as the coordinating editor responsible for obtaining suitable contributions from various authors; or we could establish a collective or institutional editorship with column responsibilities shared among a pool of contributors.”
The letter arrived in early November 1989, and Dave, I and Alfred Kleusberg, Ph.D., who was a research fellow in the department (and subsequently a professor), began to discuss whether we wanted to take on the responsibility for the column and, if so, how we would manage it. I shortly departed to the University of Bern, where I would spend the better part of two months during my first sabbatical. Communication had to take place using e-mail, although phone, telefax and telex were also possible. Universities had e-mail before most other organizations thanks to BITNET (known initially in Europe as the European Academic and Research Network or EARN), a computer network that predated the Internet. My BITNET e-mail address was lang@unb or [email protected]. As I recall, the personal part of the address was limited to at most four characters. So, when UNB joined the Internet, I basically kept the same e-mail address: [email protected]. I talked about GPS and the Internet in the November 1995 edition of the column. But I’m getting ahead of myself.
FIGURE 1: First page of Dave Wells’ notes from December 31, 1989 on how UNB would manage the “Innovation” column. (Photo: GPS World archives)
That December, the three of us more or less agreed that we would handle the column in some form. From Switzerland, I sent Dave a list of 12 possible topics for the column, but I added the rider: “Note that I am not necessarily volunteering to write any of the articles.” As we know, things turned out a little differently. During the university’s Christmas break, after I returned to Fredericton, we met at Dave’s house to discuss how we would manage the column in more detail. We met on New Year’s Eve — a Sunday afternoon — and decided that Alfred Kleusberg and I would manage the column as co-editors, with Dave serving as one of the inaugural members of the magazine’s Editorial Advisory Board. The column editorship was to be a blend of the second and third of Glen’s suggestions. The task wasn’t supposed to be too onerous. After all, the magazine was to be published bimonthly. Lots of time to get someone to write an article and for Alfred and I to edit it. Or so we thought. And the column was to be called, simply, “Innovation.” I don’t recall who came up with the name — whether it was one of the three of us or Glen, but the notes from that Sunday afternoon meeting have “Innovation” written at the top of the first page (see FIGURE 1). Ideally, as per Glen’s suggested guidelines, column articles were to be tutorial in style or written in a way that they could be understood, for the most part, by non-experts in the field.
At that Sunday afternoon meeting, we decided that Dave and Alfred would write the article for the first column. It was an introduction to GPS and some possible applications titled “GPS: A Multipurpose System.” With a couple of iterations of the article back and forth with Glen via fax (GPS World didn’t have e-mail until a few years later) and a figure delivery by FedEx, the column debuted in GPS World, Volume 1, Issue 1, January/February 1990.
It used three different positioning scenarios to explain how GPS could provide positioning accuracies from a Selective Availability-constrained 100 meters down to the sub-centimeter level. It also outlined GPS’s ability to determine platform attitude with multiple antennas and its use for accurate time transfer. There was a brief introductory couple of paragraphs, which would be a column standard (later extended to a sidebar). That first introduction went as follows:
“‘Innovation’ will be a regular column in GPS World and will comment on GPS technology, product development, and other issues and needs of the GPS community. Coordinating editors are Alfred Kleusberg, Ph.D. and Richard Langley, Ph.D. both of the Department of Surveying Engineering at the University of New Brunswick in Fredericton, New Brunswick, Canada, as is David Wells, Ph.D., co-author of this initial column.
“The first few columns will introduce GPS World readers to GPS technology. This first column focuses on the many capabilities of GPS. The next column will look at the flip side — what are the limitations of GPS? ‘Innovation’ will discuss some intriguing questions in future columns: Why is the GPS signal so complicated? How have surveyors been able to use it to get such accurate results? How serious is selective availability? We will also devote columns to exploring in depth some of the issues raised in this column: GPS and electronic charts, GPS and geographical information systems and prospects for using GPS and GLONASS together. We welcome readers’ comments and topic suggestions for future columns.”
That introduction listed the topics for the first year of “Innovation.” They were written by Alfred, me, both of us, or other researchers at UNB and, in one case, by colleagues at the Canadian Hydrographic Service. We had a very positive response to our first few column articles, so Glen kept us on, but at some point in 1990, he told us the magazine was going to 10 issues a year. There were just too many GPS-related developments to be covered in just six issues. So now there would be a monthly column except for the July/August and November/December issues.
In the second year, Alfred and I continued to write some tutorial articles for the column, but we started to invite others to submit articles, which we would then edit for style and space, and that became the tradition. Over the years, we have had hundreds of leaders in GNSS technology development and applications pen articles. In the second and third years of the column, for example, we featured articles by Stephen DeLoach on precise real-time dredge positioning, Jack Klobuchar on ionospheric effects on GPS, Edward Krakiwsky on GPS vehicle location and navigation, Yehuda Bock on continuous monitoring of crustal deformation, Keith D. McDonald on GPS in civil aviation, David Coco on GPS as satellites of opportunity for ionospheric monitoring, Derrick Peyton on using GPS and remotely-operated vehicles to map the ocean, Oscar Colombo and Mary Peters on precision long-range DGPS for airborne surveys, Adam Freedman on measuring the Earth’s rotation and orientation with GPS, Christian Rocken and Thomas Kelecy on high-accuracy GPS marine positioning for scientific applications, Marvin May on measuring velocity using GPS, Thomas Yunck describing a new chapter in precise orbit determination, and Gregory Leger on using GPS-equipped drift buoys for search and rescue operations. And the list goes on and on.
As I mentioned, in the second year of GPS World, there were 10 issues. That changed in 1993, when the magazine went to 12 issues a year, but the September and December issues were “Showcase” issues featuring more industrial news and announcements of new products. It was also to include “The Almanac” — an update on the GNSS constellations, which I also looked after. Eventually, the “Showcase” issues became regular issues but with “Innovation” replaced by “The Almanac” at the “back of the book.”
Figure 2A Different eras of “Innovation” throughout the years; the January 1993 edition (left) and the January 2000 edition (right). (Photo: GPS World archives)
The column look changed a few times over the years, typically coinciding with magazine makeovers, with the logo changing from the original 3D terrain graphic to a logo of people with stuff in their hands starting in January 1999, to a “bits” logo from January 2001, to a somewhat plain format from September 2003, with the “Insights” sidebar and my photo from April 2006, to a circle photo from November 2015, and with a new photo from January 2016. FIGURES 2A, 2B and 2C show representative column snapshots for each era.
Figure 2B Different eras of “Innovation” throughout the years; the January 2003 edition (left) and the September 2003 edition (right). (Photo: GPS World archives)
FIGURE 2c Different eras of “Innovation” throughout the years; the April 2006 edition (left) and the February 2016 edition (right). (Photo: GPS World archives)
The tutorials
As I mentioned earlier, right from the beginning of “Innovation,” we decided to have essentially two types of articles in the column: discussions of recent advances in GPS (and later GNSS) applications and related technology written by guest authors and tutorials explaining the fundamentals of GNSS including how the three main components of GNSS work: the satellites, the control segment and the user equipment. Here is a list of some of the tutorials written by the UNB team (mostly me) that were featured in “Innovation”:
GPS: A Multipurpose System (January/February 1990)
The Limitations of GPS (March/April 1990)
Why is the GPS Signal So Complex? (May/June 1990)
The Issue of Selective Availability (Sept./Oct. 1990)
Comparing GPS and GLONASS (Nov./Dec. 1990)
The GPS Receiver: An Introduction (Jan. 1991)
The Orbits of GPS Satellites (March 1991)
The Mathematics of GPS (July/August 1991)
Time, Clocks, and GPS (Nov./Dec. 1991)
Basic Geodesy for GPS (February 1992)
The Federal Radionavigation Plan (March 1992)
Precise Differential Positioning and Surveying (July 1992)
The GPS Observables (April 1993)
Communication Links for GPS (May 1993)
GPS and the Measurement of gravity (Oct. 1993)
RTCM SC-104 DGPS Standards (May 1994)
NMEA 0183: A GPS Receiver Interface Standard (July 1995)
Mathematics of Attitude Determination with GPS (Sept. 1995)
A GPS Glossary (Oct. 1995)
GPS and the Internet (Nov. 1995)
The GPS User’s Bookshelf (Jan. 1996)
Coordinates and Datums and Maps! Oh My! (with Will Featherstone; Jan. 1997)
The GPS Error Budget (March 1997)
GPS Receiver System Noise (June 1997)
GLONASS: Review and Update (July 1997)
The UTM Grid System (Feb. 1998)
A Primer on GPS Antennas (July 1998)
RTK GPS (September 1998)
The GPS End-of-Week Rollover (Nov. 1998)
The Integrity of GPS (March 1999)
Dilution of Precision (May 1999)
GPS, the Ionosphere, and the Solar Maximum (July 2000)
Navigation 101: Basic Navigation with a GPS Receiver (October 2000)
Getting Your Bearings: The Magnetic Compass and GPS (Sept. 2003)
GPS by the Numbers: A Sideways Look at How the Global Positioning System Works (April 2010); this was the 200th “Innovation” column.
As you can see, the tutorials became fewer as the years went by. As my research career expanded, I just didn’t have the additional time to write more tutorials. I had taken over sole responsibility for the column in 1997, shortly after Alfred Kleusberg left UNB to pursue a career opportunity in Germany.
However, the tutorial columns were (and still are) popular judging by the comments sent to GPS World and the number of citations for some reported by Google Scholar. For example, the one on dilution of precision has been cited in papers, theses, and reports 837 times to date. While not as many as a paper on an important medical breakthrough, it’s not a bad record for an article on a navigation topic.
Changes at the top
The column has seen four changes of editorial leadership at GPS World. Glen Gibbons, the founding editor, stepped down as editor-in-chief in July 2005 and shortly afterward started up his own publishing company to produce the magazine Inside GNSS. Alan Cameron took over the job in 2006, and subsequently became the magazine’s publisher and editor-at-large. Tracy Cozzens became the senior editor in 2019 with responsibility for “Innovation,” and then Matteo Luccio became editor-in-chief of the magazine in May 2021. I’m happy to say I got along well with all of these “bosses,” and they continued to put up with me even when I got the column in at the last moment. Additionally, the magazine’s various art directors over the years have always made the column look good.
However, after I took over sole responsibility for the column, there were no changes at the bottom. So, I’ve ended up being the longest serving GNSS rapporteur or editor, with Glen and Alan and Tracy having retired at different epochs during the past decade. In addition to the column, I have contributed a number of shorter articles to the magazine and the GPS World website over the years, sometimes joined by colleagues from different organizations, in particular the German Aerospace Center.
A bit of my own history
I wasn’t going to bother with an “Insights” sidebar for this last column. The column isn’t about a single topic that needs any background information. But you might be wondering how I got this gig as the “Innovation” editor (apart from what I’ve already told you) or got my job at UNB for that matter. So, I’m repurposing the “Insights” sidebar from the February 2016 issue of GPS World, in which I talk a bit about antenna arrays and my own radio tinkering. It doesn’t mention that after getting my Ph.D., I spent two years at MIT as a postdoctoral fellow working under the famous physicist Irwin Shapiro on analyzing lunar laser ranging data to uncover subtle changes in Earth’s rotation due to the fluctuating winds of its atmosphere. Even as a graduate student, I was involved with satellite navigation and helped to uncover a bias in the coordinate system used by the U.S. Navy Navigation Satellite System, commonly known as Transit, by comparing station coordinates with those I obtained in my very long baseline interferometry research. I’ve always been a radio nerd both in my day job and as an avid shortwave radio hobbyist. So, it is not too surprising that I got involved with GPS and then GNSS (including ionospheric studies) and established a GNSS research group at UNB with some stellar graduates over the years.
And there you have it. It only remains for me to thank all of the authors who have shared their research and understanding of the many facets of GNSS in the column over the past three-and-a-half decades, the staff at GPS World for getting the column into the print and later the electronic editions on the Web, the readers whose positive feedback encouraged me to keep the column going, and to my wife, Marg, who let me spend the long hours on the column when I should have been attending to things around the house. So, now, to paraphrase a much better journalist than I: Goodbye, and good luck.
The November 2024 issue of GPS World features Professor Richard Langley’s 300th and final “Innovation” column. His first one appeared in the January/February 1990 issue, the magazine’s very first. In celebration of Richard’s decades-long contribution to GPS / GNSS / PNT, we are publishing a selection of testimonials and photos from some of his colleagues and friends, gathered by his former students Sunil Bisnath and Attila Komjathy. Click here to read the testimonials.
IN THE BEGINNING of the space age, there was only one space-based positioning technique: satellite Doppler. Shortly after the launch of the first satellite, Sputnik 1, on Oct. 4, 1957, it was realized that by using a receiver to measure the Doppler frequency shift of a satellite’s transmitted signals combined with knowledge of the satellite’s orbit, the position of the receiver could be determined.
The United States Navy used this concept to develop the Navy Navigation Satellite System, commonly known as Transit. Although its initial use was for positioning Polaris submarines, it was released for commercial use in July 1967. Transit was used worldwide for positioning and navigation until it was decommissioned at the end of 1996. We talked about Transit in the introduction to the article “Easy Peasy, Lemon Squeezy: Satellite Navigation Using Doppler and Partial Pseudorange Measurements” in this column’s October 2012 edition.
Next on the scene was very long baseline interferometry (VLBI). This was, and still is, a technique for high-resolution mapping of galactic and extragalactic radio sources such as quasars. It was invented by Canadian and American radio astronomers with the Canadians getting the first interference “fringes” on a transcontinental baseline on May 21, 1967. VLBI uses radio telescopes, separated by 100s or 1,000s of kilometers, to record signals on storage media (previously magnetic tape and subsequently disk-based systems) synchronized by atomic clocks, typically hydrogen masers. The recordings are played back and cross-correlated at a central facility to produce the observation data – essentially the difference in arrival times of the radio signals at the radio telescopes. It was apparent that VLBI measurements could also be used to precisely determine the vector baselines between pairs of radio telescopes eventually down to a few millimeters, so VLBI became an important geodetic technique, even measuring the drift of the continents in essentially real time. We featured an article on VLBI in this column in February 1996, “The Synergy of VLBI and GPS.”
Around the same time that VLBI was being developed, satellite laser ranging (SLR) made its debut. SLR works by precisely measuring the two-way travel time of laser pulses sent from telescopes on Earth to arrays of corner-cube reflectors on specially equipped satellites. The first experiments were conducted with Beacon Explorer A in 1964. Initial results had a range accuracy of about three meters. Since then, more than 100 satellites have been launched with SLR reflectors, including the GLONASS, Galileo, BeiDou and Quasi-Zenith navigation satellites, the Indian regional satellites and a couple of GPS satellites with more to come. Ranging precisions are now as good as a few millimeters. Laser ranging is also conducted using reflector arrays on the surface of the moon. Back in September 1994, we had an SLR article in this column, “Laser Ranging to GPS Satellites with Centimeter Accuracy.”
Skipping over GNSS, with which most of us are very familiar, then came Doppler Orbitography and Radio Positioning Integrated by Satellite (DORIS). DORIS was developed in France by a group of institutions led by the Centre National d’Études Spatiales. Rather than transmitting signals from satellites and measuring the Doppler shift at receivers on the ground, the system transmits signals from a global network of ground-based beacons, which are picked up by receivers on specially equipped satellites and the data is subsequently downloaded to Earth. The first such equipped satellite was SPOT-2, launched in January 1990. Since then, 18 more satellites with DORIS receivers on board have been launched to date. DORIS, along with the other techniques, was discussed in the online GPS World article, “NASA Helps Maintain International Terrestrial Frame with GNSS,” published in February 2016.
Like the global navigation satellite systems with the International GNSS Service, the other techniques have their coordinated services, too: the International VLBI Service for Geodesy and Astrometry (IVS), the International Laser Ranging Service (ILRS), and the International DORIS Service (IDS).
All of these techniques and services contribute to the refinement of the International Terrestrial Reference Frame (ITRF), on which all positioning activities on Earth eventually depend. Tying the contributions from the different services together involves accounting for any systematic differences, which are reduced in part by using positional data at collocated sites where two or more techniques are sited with the vector ties between the instruments carefully measured. The September 1996 edition of “Innovation” was on the IERS and was aptly titled “International Terrestrial Reference Frame.”
The ITRF will enter a new era with the European Space Agency’s Genesis mission. The mission’s satellite will carry instruments for all four space-geodetic techniques: GNSS, VLBI, SLR and DORIS. In this quarter’s “Innovation” column, a team of Genesis mission engineers and scientists introduce the mission, describe its components and outline its benefits. My well-thumbed copy of the Concise Oxford Dictionary of Current English has two definitions for the word “genesis.” The first, with a capital “G,” is the title of the first book of the Old Testament with its well-known first verse. The second is “Origin, mode of formation or generation” and comes from the Greek word genēs, meaning birth, born or produced. It is clearly a fitting name for ESA’s new mission.
Some of us will remember that was how Sgt. Phil Esterhaus ended the morning roll call on the classic TV show “Hill Street Blues.” Although this warning was directed at police officers in carrying out their sometimes dangerous duties, it is good advice to anyone relying on GPS or any of the global navigation satellite systems (GNSS). Why? Although GPS and the other systems work very well in many environments, there are situations where the surroundings, such as those in natural and urban canyons, can block and reflect signals degrading or even denying positioning and navigation capabilities. And that’s not all. Space weather can also occasionally affect GPS and the other systems, limiting their use.
On top of such environmental concerns, we must worry about accidental and intentional disruptions of GNSS by radio frequency interference (RFI). GNSS signals received on or near Earth’s surface are fairly weak — much weaker than, say, cell phone signals — and so can be easily overpowered by nearby stronger radio signals. This is commonly referred to as jamming and there have been many instances of deliberate interference with GPS signal reception in North America and elsewhere. In fact, although its use by civilians is illegal, GPS jamming equipment is available that can stop a GPS-based tracking system from working.
On the other hand, GNSS signal jamming has become a common military tactic and its use is now widespread across the globe. While users might be aware that their navigation equipment is not working due to jamming, there is also the more insidious technique of spoofing, in which false GPS-like signals attempt to trick a receiver into using them rather than the true signals, resulting in an erroneous position report perhaps hundreds of kilometers away from the receiver’s true position.
While the use of GNSS jamming and spoofing can be detected on the ground and by aircraft overflying locations where the activity is taking place, these signals can be more comprehensively studied from space using satellites carrying receivers with appropriate spectrum coverage. In this quarter’s “Innovation” column, a researcher with NASA’s Goddard Space Flight Center reports on studies of GPS signal interference he has conducted using observations from a constellation of low-Earth orbiting (LEO) satellites that use onboard GNSS receivers to provide data for use in operational meteorology and the study of space weather and climate. However, the receivers also intercept jamming and spoofing signals as the satellites pass over conflict zones multiple times per day. It is in these zones and surrounding areas that all users relying on GNSS must be extra careful out there.
In 1999, professors Jordi Puig-Suari at California Polytechnic State University and Bob Twiggs at Stanford University proposed a design for a miniaturized satellite that would allow students to more easily develop the skills necessary for the design, construction, testing and operation of satellites in low-Earth orbit (LEO). These nanosatellites would be built using standardized modules with a useful volume of 10 × 10 × 10 centimeters (hence the designation cube satellite or CubeSat) with a maximum mass of 2 kilograms. Apparently, the inspiration for the design came from the plastic box used to display “Beanie Babies,” a line of small stuffed toys. While a CubeSat can be constructed using one module or unit, termed a 1U design, modules can be stacked together to form sizes of 2U, 3U and so on.
Initially just a suggested form factor, the design was widely adopted by nanosatellite developers and in 2017 the International Organization for Standardization published the ISO 17770:2017 standard to formally define the physical, mechanical, electrical and operational requirements of CubeSats.
While some CubeSats have been launched as secondary payloads on launch vehicles, many have been released into space having been first launched to the International Space Station (ISS) in a cargo resupply vehicle. For example, Nanoracks developed a CubeSat deployer that can house multiple CubeSats. Once on the ISS, the deployer is positioned so that when its forward-facing door is opened, a spring at the back of the deployer pushes the CubeSats into space.
As of January 1, 2024, 2,323 CubeSats have been launched according to a nanosatellite database. Some of these satellites demonstrated new space technologies while others were science investigation missions to study Earth’s atmosphere or space weather or astronomical objects or other satellites. Many of these CubeSats, if not most, have been built by universities from around the world. In fact, various space agencies have programs to support the development and launch of CubeSats by students, such as NASA’s CubeSat Launch Initiative and the Canadian Space Agency’s Canadian CubeSat Project (CCP). As most CubeSats go into LEO, a lot of them have already deorbited. However, while in space, they provided a wealth of data of various kinds and many of the accumulated datasets are still being mined for new results. A nice example of such a dataset is that provided by Bobcat-1, a 3U CubeSat developed by Ohio University. Its mission, in addition to training students in aerospace technologies, was primarily to assess the feasibility of monitoring the time offsets between different GNSS, but also GNSS spectrum monitoring and testing a software-defined GNSS receiver. In this quarter’s “Innovation” column, authors from the Bobcat-1 team discuss some of their work on Galileo-to-GPS system time offsets. Go Bobcats!
This is an introduction to the November 2023 Innovation article, “Using GNSS Phase Reflectometry on Maui’s Haleakalā”
We’ve all seen the news reports of the terrible devastation and loss of life in the town of Lahaina on the island of Maui by a wildfire this past August. Those terrible reports jarringly contrasted with happy memories of visits to Hawaii and its paradise islands. I recalled my visit some years ago to Maui in particular. My wife and I traveled all around Maui, but we particularly enjoyed the drive up to the top of Mount Haleakalā.
Rising to just over 3,000 meters, Haleakalā is a large, active (though currently dormant) shield volcano that forms about 75% of Maui. Just below its summit there is a visitor center with informative panels describing the geology of the volcano and the flora and fauna to be found on its flanks. On the drive up, for example, you can see endangered nēnē, the Hawaiian Goose, and the threatened silversword plants, which only bloom once in their lifetimes. And the sunrise and sunset views from the summit are quite beautiful.
A few hundred meters away from the visitor center is the Haleakalā High Altitude Observatory Site — a complex informally known as “Science City.” The site accommodates various optical telescopes and other instruments, including among others the 4-meter-aperture Daniel K. Inouye Solar Telescope (the largest solar telescope in the world), a satellite laser ranging station, and the Maui Space Surveillance Complex, which consists of a suite of telescopes operated by the Department of Defense for satellite tracking.
Also at the site is an innovative system observing the ocean surface far below using the phase of GNSS signals. Not only receiving normal line-of-sight signals from satellites, this system also receives signals that are reflected by the ocean surface, a technique called GNSS reflectometry or GNSS-R. GNSS-R can be thought of as a bi-static radar, where the transmitters (the GNSS satellites) and the receiver are separated by a large distance. The receiver can be on Earth’s surface, on an aircraft or on a low-Earth-orbiting satellite. The reflected signals contain information about the surface from which they were reflected. Depending on the receiver’s location and with suitable data processing, parameters such as ground surface elevation and its variation, water level and tide height, sea state (wave height, wind speed and wind direction), soil moisture content, and even snow depth can be deduced.
Over the years, we have had a number of articles on GNSS-R in this column using different receiver platforms (April, 1999; October, 2007; October, 2009; September 2010; September 2014; and October, 2019). In this quarter’s “Innovation” column, we have an article by some members of the team who built and operate the GNSS-R system on the top of Haleakalā. They explain how the system works and some of the preliminary observations and results they have obtained. More science in paradise!
On October 25, 1997, a defense satellite was launched from Cape Canaveral on an Atlas rocket with a Centaur upper stage. The Centaur went into an elliptical geosynchronous transfer orbit with an apogee close to geostationary orbit radius before releasing the satellite. Bolted to the side of the Centaur was an instrument package containing a GPS digital sensor. This piggyback device was part of an experiment by students at the U.S. Air Force Academy to test some of the concepts of GPS navigation for high-altitude spacecraft.
The sensor captured 40-millisecond samples of GPS L1 signals collected by a patch antenna. The digital samples were downlinked to a ground station in Colorado Springs where they were subsequently processed. The equipment successfully operated from November 3 until at least November 9. During that time, GPS signals were detected across a wide range of altitudes above the GPS constellation including at times when the Falcon Gold antenna was only in view of a GPS satellite’s transmitting antenna sidelobes. The downlinked data was carefully archived. The Falcon Gold experiment was discussed by Thomas Powell of the Aerospace Corporation in an article he wrote for this column in October 1999 entitled “The View from Above: GPS on High-Altitude Spacecraft.”
Fast forward a couple of decades. Researchers at the University of Minnesota are taking a fresh look at the Falcon Gold data using some innovative analysis tools, which may prove beneficial for processing GNSS data from receivers on other satellites flying above the GNSS constellations even all the way to the Moon. In this quarter’s Innovation column, they tell us about their work and its potential benefit.
This Falcon Gold data study is a great example of how archived GNSS data can be reanalyzed with fresh insight and new techniques to milk even more and better results from the data. Another important example is the wealth of data that has been acquired by the International GNSS Service since beginning operations in 1994. The data in the archive is reprocessed from time to time to produce a more consistent long product set for analysis of sources of systematic error and to improve its ultimate accuracy. This results in a better understanding of Earth system dynamics, for example, including plate tectonics. The data from many other GNSS instruments flown in space is also archived allowing look backs for further and more detailed analyses. This includes my GPS Attitude, Positioning, and Profiling (GAP) instrument on the CASSIOPE scientific satellite, now part of ESA’s Swarm constellation. Researchers continue to produce interesting scientific results from the GAP data. So, it’s not always necessary to generate fresh data for a study – useful data may already exist. What’s old can indeed be new again!
While I’m likely preaching to the choir here, GNSS cannot work unless we have an accurate description of the orbits of the satellites and the behavior of their atomic clocks. The accuracy with which this information is provided to a receiver or data processing software is the most important component of the error budget of GNSS positioning, navigation and timing and constitutes most of what is known as the signal-in-space (SIS) range error.
Each GNSS satellite broadcasts a description of its orbit or ephemeris along with the offset of its active clock from the system’s time standard in a navigation message decoded and used by the receiver. These data are predictions of the orbit and clock offset as computed by the system’s ground control segment and uploaded to each satellite. A recent assessment by U.S. Space Systems Command of the GPS SIS error averaged across all active satellites for a one-week period was about 50 centimeters, root-mean-square. While this is entirely adequate for many GNSS uses, it falls short of the required accuracy for high-demanding applications such as surveying, geodesy, atmospheric sensing, reference frame studies and tectonic monitoring. Which is why various organizations both private and public compute very accurate orbits and clocks and provide these to users. These computations, using data from global receiver networks, are very exacting and model the tiniest effects on the (primarily) carrier-phase measurements these receivers provide.
These effects include the offset in the electrical phase centers of a GNSS satellite’s transmitting antenna from the satellite’s center of mass and how that varies with the direction of the signal from the satellite to a receiver on Earth. Furthermore, this behavior must be calibrated and modeled for each radio frequency that the satellite transmits. Another effect that must be accounted for are the perturbations caused by non-perfect yaw-steering of a satellite’s solar panels. These panels continuously track the Sun but they have difficulty keeping up at orbit noon and midnight. Accurate models of the actual yaw angle are very important for high-precision GNSS orbits. As if these model requirements were not enough, the effect of solar radiation pressure on satellite orbits must also be modeled. While they don’t have (rest) mass, photons have energy and this can be imparted to satellites when they impinge on them. While a single photon has a negligible effect, the billions upon billions of photons making up sunlight do have a noticeable effect on a GNSS satellite’s motion and must be accounted for by orbit models.
One organization producing precise orbits for GNSS satellites – arguably the most precise in the world – is the International GNSS Service (IGS), a voluntary federation of more than 200 agencies, universities and research institutions across the globe. Several of these organizations each produce precise orbits, which they submit to the IGS to establish orbit products. One of these organizations is the Navigation Support Office (NSO) at the European Space Agency’s European Space Operations Centre. In this quarter’s Innovation column, a team of NSO engineers discusses how they have improved the orbit modeling of the GPS III satellites by around a factor of two with estimated orbit errors of about 2 centimeters or less. Wizardry? Not really – just rocket science.
WHAT IS CARRIER PHASE? The obvious answer is: the phase of the carrier. But this is not helpful if you don’t know what a carrier is. A carrier is basically a harmonic electromagnetic wave — a pure continuous sinusoidal wave with a single constant frequency and amplitude.
Such a wave has limited uses. However, if we modulate or change the characteristics of the wave in some way, then the wave can carry information. Changing the amplitude by using a voice or music audio signal is amplitude modulation as used for AM radio.
Instead, one could modulate a carrier by changing its instantaneous frequency, which is frequency modulation or FM and is used for high-fidelity broadcasting. Yet another way to modulate a carrier is to change the instantaneous phase of the carrier, and that is how GNSS works.
GNSS carriers are phase-modulated by pseudorandom noise (PRN) codes and navigation messages. A GNSS receiver uses the PRN codes to produce the pseudorange observable with a precision in the tens of decimeter range. This is the most common observable for GNSS positioning.
But by stripping away the modulation of the received GNSS signals, the receiver can measure the phase of the underlying carrier. Changes in carrier phase over time reflect the change in the (pseudo)range but are about two orders of magnitude more precise.
One problem with carrier-phase measurements is that they have an initial cycle ambiguity that must be resolved, preferentially fixed to the correct integer value, before they can be used for positioning, but this can be achieved without too much difficulty. While fixing the ambiguity of carrier-phase measurements might be considered a nuisance in GNSS positioning, it can help detect spoofing of GNSS signals where some other techniques might fall short.
In this “Innovation” column, we look at how carrier-phase measurements combined with those from an inertial measurement unit can guard against a deliberate attack on an automated ground vehicle — something that cannot be discounted in our world these days.
A Step-by-Step Exposition of an Educational Resource
Innovation Insights with Richard Langley
THE RADIO. It’s been around for more than 100 years. Pioneering work by Guglielmo Marconi and others in the1890s and 1900s resulted in practical wireless telegraphy devices that permitted point-to-point communications with ships at sea and between stations on land hundreds and thousands of kilometers apart and even between stations on different continents. The first radio broadcasts (point-to-multipoint) were time signal transmissions and weather broadcasts. Experimental audio transmissions took place in the early 1900s, and by 1920 or so, radio stations were established in many countries for broadcasting speech and music to the general public.
The first radio receivers were simple crystal sets. It wasn’t until the mid-1920s that tube radios became commercially available. Eventually, tubes were replaced by transistors, and transistors by integrated circuits. The introduction of microprocessors resulted in digital receivers, with the conversion of the received analog radio signals into audio being carried out digitally for the most part.
One of the latest advances in radio technology is the software-defined radio or SDR. An SDR typically consists of two components: a piece of hardware, called a radio frequency (RF) front end, and a piece of software run on a general-purpose computer. The job of the front end is to convert a portion of the radio spectrum received by its antenna to a digital data stream processed by the software. The software decodes the data to produce the desired result. Since the software does most of the “heavy lifting” in processing a radio signal, it is often called the SDR itself. And by the way, there are SDR transmitters, too.
It should come as no surprise that SDR technology has come to the GNSS field. In fact, in 2007, the seminal text on GNSS SDRs, A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach, was published along with the sale of an inexpensive RF front-end in a thumb-drive-sized package that allowed graduate students and others to experiment with a GNSS SDR themselves. And we have covered GNSS SDR developments in this column from time to time, most recently in January 2018 (“The Continued Evolution of the GNSS Software-Defined Radio: Getting Better All the Time”).
In this month’s column, researchers from the lab that helped produce the SDRs documented in the 2007 book (which is still in print) discuss their development and testing of additional freely available SDR codebases covering all four GNSS (GPS, Galileo, BeiDou and GLONASS). They provide an excellent resource for learning how GNSS receivers actually work.
By Joan Bernabeu, Nicolas Gault, Yafeng Li and Dennis M. Akos
With the publication of the book A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by Kai Borre, Dennis Akos and their fellow authors, an open-source GNSS software-defined radio (SDR) receiver developed using Mathwork’s Matlab language was made available, together with sample data sets that facilitated the testing process for all interested readers. The first SDR implementation focused on processing the GPS L1 C/A-code legacy signal and served as a starting point for students and researchers in the Radio Frequency (RF) and Satellite Navigation Laboratory at the University of Colorado Boulder, where later activities aimed to improve the software code and add new features as new GNSS signals emerged. As a result, the initial codebase evolved into a complete collection of SDRs capable of processing all GNSS signals from every satellite constellation, with BeiDou’s B1I, B1C, B3I and B2a signals the latest additions. The most recent efforts were dedicated to collecting all SDR codebases, putting them in a common format, and testing them to give an account of their performance. This article describes our efforts, placing special emphasis on explaining the test framework designed to test each SDR, as well as on reporting the adjustments made and the results obtained. GPS test cases have been taken as examples to show how some SDRs were assessed when issues were found in the results they provided.
OPEN-SOURCE GNSS SDR COLLECTION
The whole SDR collection has been developed in Mathwork’s Matlab programming language. To run the code and perform tests, users simply require an active Matlab license and the software available on their computer. Once these requirements are met, the user can choose to download any of the available codebases and the corresponding data set to start experimenting.
We recommend using version control software to keep track of changes made to the original version of the code. Users should consult the Borre et al. text for further details on running the codebases.
A total of 12 SDR codebases are aimed at processing each of the GNSS signals (see TABLE 1). All code files for each SDR are organized in the same subdirectories, and most of them have the same filenames.
Table 1. All GNSS signals that can be processed by the SDR collection, organized by their corresponding satellite systems.
All SDRs are set to work with a default configuration. They are all run using an init.m script, which collects user settings (input data file path, sampling frequency and so on) from the initSettings.m configuration script. Given this, the first file that users may want to modify is initSettings.m, to define the run settings for a given test. Most of the SDRs operate in an identical way, however some include particular features oriented at exploiting certain characteristics of the corresponding GNSS signal. The GPS L2C SDR, for example, gives the user the option of whether to process the pilot component of the signal.
The test samples available in the public directory were obtained in accordance with the characteristics depicted in TABLE 2 for every signal. The first two columns from the left show all the signals the SDR collection can process and the central carrier frequency at which they are transmitted. The third column gives the bandwidth selected in the recording process for every signal. This value must match the sampling frequency defined in the initSettings.m file for each SDR. Only three frequency bandwidths can be used to record GNSS data, so as to make the configuration structure more homogeneous across different SDRs. They were selected to ensure similar characteristics for each signal in terms of performance, encompassing most of the signal power for each modulation, but also keeping the recorded GNSS data files within a reasonable size.
Table 2. Summary of the tested GNSS signals’ center frequencies and the selected bandwidth (BW) for their processing. The common IF for all signals is 20 MHz.
All the signals were mixed to a common intermediate frequency (IF) of 20 MHz in the recording process. Both the frequency bandwidth and the IF are fundamental to obtain the expected results from each SDR codebase. These are set in the settings file. The default configuration was validated in the testing campaign explained in later sections, and should only be modified to meet the user’s specific needs, being aware that some SDR performance characteristics may also be affected.
BASIC GNSS SDR STRUCTURE
While the general SDR receiver structure is similar across all codebases, each comes with adjustments and/or additions to adapt the code to the format of a specific signal. The general codebase structure can be summarized in four major modules:
signal acquisition
tracking stage
navigation data decoding
position, velocity and time (PVT) computation.
An important remark is that the SDR collection developed is designed to process files of limited duration. The code is designed to use enough data to provide a successful initial acquisition, and then use a single set of satellites for the remaining execution. In other words, there is no extra logic oriented at acquiring or reacquiring satellites after the first acquisition is achieved.
Signal Acquisition.The design of the acquisition scheme depends on the characteristics of the signal the SDR is aiming to process. There are numerous GNSS signal configurations for each constellation that follow different strategies concerning spreading codes, navigation data and secondary codes, which must be accounted for in the acquisition codebase.
All codebases follow a fast Fourier transform (FFT) accelerated serial search-acquisition approach to obtain estimates of the signal’s carrier frequency and code delay, where a number of signal replicas are generated iteratively, separated by a defined frequency interval in the frequency domain. All the frequency offsets are arranged in what are known as frequency bins. This frequency separation will be referred to as the frequency step. The latter is inversely proportional to the integration time and tells the maximum error allowed in the carrier frequency estimate, which is half the frequency step. Both the frequency step and the coherent integration time are parameters that have a strong effect in acquisition results, as will be seen below.
Each local replica is correlated with the input signal to obtain a code-phase estimate. The length of this correlation is the so-called coherent integration time. The maximum correlation measurement from all frequency bins is then divided by the second maximum found. This ratio is called the peak metric and is used in all SDRs to give a measure of the magnitude difference between the maximum obtained and the remaining correlation results. If the peak metric is not high enough, this implies that the maximum is close to other cross-correlation products and so could not correspond to the result obtained after correlating both input and local replica signals with the right code-phase alignment. When the peak metric surpasses the threshold defined in initSettings.m, the satellite is considered to be acquired.
It is worth noting that in all SDR implementations the local replica is constructed by concatenating a whole primary code and a block of zeros of the same length. This prevents navigation bit transitions from affecting the correlation results. For example, GPS L2C-CM SDR’s acquisition correlates 40 milliseconds of data with 20 milliseconds of pseudorandom noise (PRN) spreading code followed by 20 milliseconds of zeros (the zero padding technique).
Tracking Stage.The tracking stage is oriented at refining and keeping track of the code and carrier estimates provided by the acquisition stage as well as demodulating the navigation data. This is achieved using feedback loops organized in channels, which are typically referred to as tracking channels. There will be as many tracking channels as the number of satellites acquired. Each tracking channel makes adjustments to the corresponding local signal replica for the given satellite, so that it resembles the real received signal as much as possible. When the replica is sufficiently accurate, the tracking loop locks onto the signal, removes the carrier and spreading code components, and starts registering data bit transitions. The task of every tracking channel is to account for signal variations so that they can keep locked on the signal for as long as the satellite is available for use.
Tracking channels implement two feedback loops, the delay lock loop (DLL) and the Costas phase lock loop (PLL). The former is focused on the signals’ code phase while the latter on the carrier phase. These modules depend on two major parameters that determine the properties of the loop filter: the damping ratio and the noise bandwidth. On the one side, the damping ratio controls how fast the filter reaches the settling stage. On the other side, the noise bandwidth informs the amount of noise allowed in the filter.
While all SDRs follow similar tracking loop schemes, some signals, such as GPS L2C, need some adjustments to the parameters mentioned above so that they provide the expected results, as we point out later. Tracking results are stored in a Matlab .mat file, but also can be assessed in the plot the tracking stage generates after it finishes processing all the channels.
FIGURES 1a and 1b show an example of two different tracking results plots, each of which include seven figures. These show the in-phase/quadrature (I/Q prompts), the navigation data bits decoded, the changes in the raw/filtered Costas loop and DLL discriminators, and the early-prompt-late metrics. Note that the plots in Figure 1a suggest the navigation data bits were demodulated successfully. In contrast, in Figure 1b, data bits cannot be distinguished because the tracking stage failed to demodulate the navigation message.
Figure 1. (a) shows the plot generated for a successful tracking channel. In contrast, (b) illustrates the results obtained when the tracking loop in question did not lock appropriately to the signal and therefore was not able to demodulate navigation data. (Image: Authors)
Navigation Data Decoding.This stage extracts the navigation data required by the SDR codebase to compute PVT estimates from the results delivered by the tracking stage. The latter outputs I/Q prompt samples representing data bits, containing the encoded navigation data. The navigation data format for each signal can be found in the interface control document issued by each satellite constellation operator.
The general process that each SDR implements to demodulate navigation data from I/Q samples is summarized as follows:
detect a preamble within data bits
arrange the bit sequence in the corresponding structures, such as frames
remove secondary code if present
de-interleave and decode
check if the bit stream has errors
extract navigation parameters
Once navigation parameters are extracted, they are stored and later used by the functions involved in the PVT computation stage.
PVT Computation.The PVT stage takes the decoded navigation data, computes satellite positions, and solves the geometry problem, whose solution is the receiver’s location.
As with all the other stages, all SDRs follow the same approach, and use the least-squares method to solve for a position estimate once all the data is available. Position estimates are delivered in both Earth-centered Earth-fixed and east-north-up coordinates.
Similarly to the tracking stage, the PVT computation stage returns a plot showing some PVT statistics to help the user get an idea of the PVT performance of the test conducted. FIGURES 2aand 2b show an example of two positioning plots obtained for two different data files.
Figure 2. (a) shows the plot of a priori, good statistics for the navigation solution; (b) shows a navigation plot for a file that presented a problem affecting the PVT solution. (Image: Authors)
EXPERIMENTAL SET-UP AND TESTING
In this section, we present the equipment we used in our tests (see FIGURE 3) and detail the process we followed to collect GNSS data, as well as the testing framework designed to exercise the SDR collection.
Figure 3: The antenna was connected to the RF port of the USRP. The USRP sampled the analog data delivered by the antenna using the TCXO as the reference oscillator. The resulting sampled data was stored in a Linux-based computer. (Image: Authors)
RF Antenna.The device used to sense the RF GNSS signals was a Trimble Zephyr2 antenna, which has enhanced capabilities for multipath minimization as well as low-elevation-angle satellite tracking properties.
The antenna was installed on the rooftop of the Ann and H.J. Smead Department of Aerospace Engineering Sciences building at the University of Colorado Boulder.
USRP and TCXO Devices.An Ettus Universal Software Radio Peripheral (USRP) B200 hardware SDR connected to an IQD temperature-compensated crystal oscillator (TCXO) was used to collect digital samples from GNSS analogue signals sensed by the antenna.
The B200 device was controlled by means of the USRP hardware driver (UHD) through a computer running a Linux operating system. UHD is a software application programming interface (API) that enables the development of code to manage USRP settings and operation.
PC Setup.The PC setup consisted of a Linux computerwith all the required drivers and program dependencies, as well as with Mathworks’ Matlab software installed. Matlab was used to program and automate the data recording process.
Recording Process.The equipment described in previous subsections was used to record data suitable for each SDR codebase. The process to obtain signal data for all 12 codebases was reduced to eight stages by selecting an adequate frequency bandwidth, as some signals share the same central carrier frequency (see Table 2).
For each stage, a total of 100 files with 61 seconds of I/Q GNSS data were recorded over a 24-hour time period. The I/Q samples recorded by the USRP were formatted as 8 bit sine carriers. All the data sets recorded are available together with a description file based on the Institute of Navigation’s metadata standard for GNSS.
TESTING FRAMEWORK
The workflow we followed to test every codebase from the collection is outlined in the following steps:
Record data samples.A set of one hundred files were recorded with 61 seconds of GNSS data.
Debug the SDR with the selected files. A debugging stage preceded every test case to ensure the codebase performed well enough, or else to make the required adjustments.
Run the SDR for one hundred trials. A total of one hundred tests, one per file, were performed for all SDRs.
Log metrics and present results.The results from all SDR stages (acquisition, tracking and data demodulation) were stored for each file. Also, each iteration returned a message that summarized the execution results.
All of the messages returned for every file corresponded to one of the cases summarized as follows:
Codebase issue. Message type returned when the codebase failed because of a coding issue.
No navigation solution. The codebase was not able to deliver a navigation solution either due to a malfunction of the codebase or due to a lack of satellite availability. Navigation solutions are only available when both the tracking channel and the navigation data demodulation stages are successful for more than three satellites.
Navigation solution with accuracy worse than 30 meters. A position solution fix more than 30 meters (in three dimensions) from the known antenna location was considered a non-accurate estimate.
Navigation solution with accuracy under 30 meters.When the 3D positioning error was < 30 meters, the navigation solution for the position was considered accurate.
All codebases passed a debugging stage before being tried with the whole set of available data. This was done to ensure that they performed as expected, and were able to achieve the required performance in terms of the metrics mentioned above in this section. An example of this debugging stage will be explained in further detail below. We take the GPS L2C codebase as an example of how all implementations were assessed in an attempt to improve their initial performance and make them more robust to code errors. See our proceedings paper for further details of our test cases.
GPS L2C Test Case.The problem observed for GPS L2C was that some satellites acquired with a high acquisition metric were failing the tracking stage. The result was that no navigation data was demodulated from them. An in-depth study was required to find out the adjustments needed in the codebase that would help to solve this issue.
The GPS L2C signal encompasses two signal components called civil moderate (CM) and civil long (CL). The CM component is formed by a spreading code that modulates a navigation message. The CL component is a pilot (data-less) signal modulated with a longer spreading code allowing for longer coherent and non-coherent integration times, yielding better sensitivity.
For CM signal acquisition, the 20 millisecond code length limits the coherent integration time to 20 milliseconds, due to the overlaid navigation message. This integration time defines the minimum frequency resolution required to obtain the expected correlation results. The CL component is used in the SDR to accumulate consecutive correlation results non-coherently, contributing to the receiver’s sensitivity by allowing it to operate with higher acquisition metrics in general.
The initial configuration for this SDR codebase is represented in TABLE 3a.
Table 3. Configurations for GPS L2C test case.
With this configuration, a total of 10 satellites were acquired. However, it was observed that for some satellites acquired with high peak metrics, it was not possible to demodulate their navigation data, and thus they were not considered for the navigation solution in later stages. This situation was abnormal, as typically this behavior is more characteristic of weaker signals whose bit transitions are too noisy to be decoded. This problem suggested that either the code-phase or the carrier frequency estimates (or both) were not accurate enough for each tracking channel to generate a proper replica to lock onto the input signal.
The first step taken to address this matter was to inspect the SDR’s acquisition stage for a file presenting the mentioned problems. For instance, taking a closer look at the carrier and code-phase 3D representation for those satellites acquired with a high acquisition metric that were not successfully tracked afterwards. After doing so, some satellites were identified with the irregular characteristics described above, as for example the PRN 10 satellite. PRN 10 is taken as a reference throughout this subsection.
The metric analyzed for PRN 10 was the matrix built by the acquisition’s serial search process. This matrix contains the correlation results obtained for each frequency bin. The width of each frequency bin is determined by the frequency step size defined in the configuration file. In this way, the smaller the frequency step, the more frequency bins that the corresponding matrix contains. This implies a better frequency resolution.
With this in mind, the frequency resolution was progressively increased by decreasing the frequency step size. Extra logic had to be added to the acquisition algorithm to implement this feature. It was found that when using a step size of 6.5 Hz, the tracking stage was then able to lock and demodulate navigation bits from PRN 10 effectively. This was the most significant determining factor to overcome the issue in question for the majority of the satellites available. However, other smaller adjustments also improved tracking results in general. These are depicted in TABLE 3b.
CODE AVAILABILITY
All the resources concerning the SDR collection are publicly available at the portal hosted by University of Colorado Boulder. Through this portal, all the GNSS codebases along with the data sets for testing can be acquired, as well as access to the discussion forum.
CONCLUSION
The first version of the SDR collection was made available after the seminal text by Borre et al. was published and consisted of a GPS L1C/A SDR and multiple data sets. From then on, this project kept evolving by adding more SDRs as new GNSS signals emerged across different satellite constellations.
Our most recent work was to collect all the SDR codebases, arrange them in a common format, and test each implementation to assert their robustness and extract statistics concerning their performance.
Future work will be dedicated to adding more features aiming at refining the PVT estimates delivered by each SDR.
More progress is expected to be made soon, with additional improvements made in the GNSS laboratory. In addition, there is plenty of room for contributions from other researchers who want to support and collaborate with this open-source initiative. Our portal provides a convenient way to manage these contributions.
ACKNOWLEDGMENTS
We thank the many individuals who collaborated in the development of the open-source GNSS SDR collection.
This article is based on the paper “A Collection of SDRs for Global Navigation Satellite Systems (GNSS)” presented at ION ITM 2022, the 2022 International Technical Meeting of the Institute of Navigation, Jan. 25–27, 2022.
JOAN BERNABEU is a Ph.D. student at the Institut Supérieur de l’Aéronautique et de l’Espace, Toulouse, France. He also works as a satellite navigation engineer for GMV, Spain.
NICOLAS GAULT is a Ph.D student at École Nationale d’Aviation Civile, Toulouse, France. He was a visiting scholar in the Department of Aerospace Engineering Sciences at the University of Colorado (CU) Boulder in 2020-2021.
YAFENG LI is an associate professor in the School of Automation at the Beijing Information Science and Technology University, China. He was a visiting researcher with the Department of Aerospace Engineering Sciences, CU Boulder in 2017–18.
DENNIS M. AKOS is a faculty member in the Department of Aerospace Engineering Sciences at CU Boulder.
AS CAT STEVENS (yes, he’s back to using his old name) famously sang on “Wild World”:
“… take good care Hope you make a lot of nice friends out there But just remember there’s a lot of bad and beware Beware.”
While he was talking about a girlfriend leaving him, the warning can just as well apply to GNSS users — especially those relying on GNSS for safety-of-life navigation and the maintenance of critical public infrastructure systems.
GNSS signals are relatively weak and they are susceptible to unintentional and intentional jamming that can make reception of the signals difficult or impossible. The jamming of radio signals to hinder reception is nothing new. It’s been used by those wanting to interfere with the use of the radio spectrum ever since radio became an important tool for communication and navigation in the early 20th century. Jamming has been used in hot wars to try to defeat military communication as well as in cold wars to try to prevent a perceived enemy from broadcasting to a particular country’s citizens. Notably, the shortwave radio broadcasts from Western countries were jammed by the former Soviet Union. And even today, broadcasts directed at China, Cuba and some other countries are regularly jammed.
GNSS is also being intentionally jammed on a regular basis in some parts of the world for various purposes including the protection of politicians and civilian infrastructure and to foil GNSS-guided munitions. But while directed at supposed threats, the jamming affects all GNSS receivers in a certain radius of the jammer. Such jamming activities are being reported in the popular press with an increasing frequency.
While GNSS jamming is receiving increased attention in our troubled world, even more pernicious is GNSS spoofing. Spoofing is the attempt to mimic GNSS signals to try to trick a receiver into tracking them and thereby compute a wrong position and/or time at the receiver. This can have disastrous consequences if not detected immediately and the use of GNSS deactivated.
So, how do you detect GNSS signal jamming and spoofing? We have discussed this issue in several columns over the years, but in this month’s column, a team of researchers from Stanford University and the University of Colorado describe how they are using relatively inexpensive equipment and sophisticated software and analyses to detect and warn of GNSS jamming and spoofing. Clearly, they are heeding Cat Stevens’ warning.
By Leila Taleghani, Fabian Rothmaier, Yu-Hsuan Chen, Sherman Lo, Todd Walter, Dennis Akos and Benon Granite Gattis
GNSS signals are extremely low power by the time they reach users on Earth and are easily overwhelmed by nearby terrestrial signals. Such signals can interfere with a user’s ability to receive the desired GNSS signals or, even worse, replace them with simulated signals that cause the user to obtain the wrong position or time estimate. Two major types of radio-frequency interference (RFI) threats have been identified: jamming and spoofing. Jamming results from emissions that do not mimic GNSS signals, but interfere with the receiver’s ability to acquire and track GNSS signals. Spoofing is the emission of GNSS-like signals that may be acquired and tracked in combination with, or instead of, the intended signals.
Both threats have been studied at length by researchers, and their presence around the globe has been reported even in the popular press. Some research has been done into the prevalence of spoofing. Even so, there is no well-developed understanding of how widespread these threats are.
Terrestrial interfering signals may be fairly weak and only effective in a limited area. Complex environments with buildings or terrain may further limit their effective area of influence and hinder the ability of external interference detection. To create a better understanding of the presence and characteristics of jamming and even spoofing, we are developing a low-cost RFI detector based on a commercial, off-the-shelf GNSS receiver: the u-blox F9. We are pairing this receiver with a Raspberry Pi computer and are developing custom software to monitor the receiver outputs and store data surrounding interesting events.
We are developing a toolset in MATLAB and C/C++ with the intention of processing and analyzing the u-blox data. The toolset includes functionality to decode selected u-blox messages that contain parameters of interest. These metrics include automatic gain control (AGC), carrier-to-noise-density ratio (C/N0) and spectral power. They also include raw pseudoranges from multiple constellations and internal u-blox interference metrics. With the volume of data that can be gathered from continuous monitoring, we have begun characterizing nominal performance and developing approaches to spoofing and jamming detection. The publicly available code can be accessed through our Git Repository at https://github.com/stanford-gps-lab/navsu.
With the raw pseudoranges and downloaded broadcast ephemeris data, we compute navigation solutions using different combinations of constellations and frequencies. When the individual and multi-constellation position solutions are compared to each other, discrepancies can be flagged and investigated for possible interference. We have begun characterizing nominal power metrics such as AGC and C/N0. With the quantity of data that we can get from the RFI monitor, we are working to characterize other receiver-specific parameters such as the u-blox continuous wave (CW) jamming indicator. We leverage data collected under nominal and jammed conditions to understand and identify a threshold for what can be considered interference.
Many different methods have been proposed for GNSS interference detection and mitigation with large-scale data at multiple locations. In this article, we present our data-selection process, our development of thresholds for determining interference, and results from three u-blox receivers set up at different locations in the United States to glean information about nominal (non-spoofed) conditions. We inform our thresholds and analysis tools using datasets from nominal conditions, and then compare their performance to a dataset containing RFI events from a government-sanctioned jamming and spoofing test. Our results display how we leverage simple and powerful metrics informed by a low-cost receiver to understand nominal noise environments and successfully identify jamming and spoofing events.
Data and Metrics
We collect and analyze a variety of data types and metrics to help identify and characterize jamming and spoofing occurrences. The receiver model we started with, u-blox ZED-F9P-02B, can monitor two different RF bands and many signals, including GPS L1C/A, L2C; GLONASS L1OF, L2OF; Galileo E1B/C, E5b; BeiDou B1I, B2I; QZSS L1C/A, L1S, L2C; and SBAS L1C/A. It has 184 channels, which can be configured to sweep through an array of signals to be monitored. We are also developing monitors based on the recently released ZED-F9T-10B, which is capable of L1 and L5 signal reception. TABLE 1 describes which version of the u-blox receivers each dataset comes from.
TABLE 1. Locations of u-blox monitor for nominal noise environment characterization and jam/spoof test. (Data: Authors)
L1 and L5 are the primary frequencies used for aviation, hence a monitor for these frequencies would be more useful for protecting aviation than the F9P, which is only capable of L1 and L2 reception. The available data includes raw measurements such as code and carrier phase, position estimates, power level estimates including C/N0, AGC and spectral power. It also has active CW interference detection. These metrics are all necessary for the consistency checks and power monitoring methods we summarize in this article. Consult our conference proceedings paper for details (see Acknowledgments). By examining all of these signals and measurements, we can observe changes in the RF environment and detect inconsistencies in the received signals.
Data Logging. The u-blox receiver logs messages in a specific format. The message types important to log are selected based on the desired data. Due to limited bandwidth, we prioritized messages that efficiently include all desired parameters for the interference detection methods we describe in this article. We have used both the u-blox F9P and the u-blox F9T.
To characterize nominal noise environments, u-blox receivers were set up at three locations: Stanford University, the University of Colorado (CU) in Boulder, and at the Colorado Springs airport. All measurements from satellites below an elevation angle of 5 degrees were ignored. The results from these locations are summarized below. Results from a jamming/spoofing test sanctioned by the U.S. Department of Homeland Security are presented and labeled with the acronym “GET-CI” (GPS Testing for Critical Infrastructures) in the subsequent discussion. Table 1 describes the parameters of the u-blox receiver at each location.
Positioning Metrics Development.The nominal error of the single- and multi-constellation position solutions is made by noting the difference between the computed position and the known truth. The inter-constellation consistency check is defined as the difference between the positions computed from two constellations, with no reference to a known truth position. To analyze the nominal differences in the north, east and down (NED) directions, we use the position covariance matrix, R, computed in the least-squares solver, to set a covariance-bound threshold. The covariance for each constellation is assumed independent. We present our results using this threshold in our results sections.
Our results in FIGURE 1 show that the Galileo position solution variance is higher than the dual-constellation and GPS-only solution. This is attributed in part to the fact that Galileo, while operational, has not filled out all planned satellite slots and therefore has fewer satellites and worse geometry than GPS.
FIGURE 1a. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Colorado Springs. (Image: Authors)
FIGURE 1b. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at CU Boulder. (Image: Authors)
FIGURE 1c. Map visualization of the comparison among position solutions computed using only GPS, only Galileo and a combined GPS plus Galileo dual-constellation solution at Stanford. (Image: Authors)
Nominal Noise Results
Here are some of our positioning and power monitoring results under nominal reception conditions.
Positioning. Based on the methods described earlier, we present a selection of our results from the positioning consistency checks. We present several informative visualizations of the error between the computed position solution and the known truth of each u-blox receiver and use the covariance threshold to bound the raw error. The error for dual-constellation, single-constellation and inter-constellation consistency checks are all displayed and compared to one another. The pseudorange residuals and their accompanying chi-squared (χ2) statistic are also evaluated and compared for the GPS and Galileo single-constellation position solutions.
Positioning Consistency Comparison Maps. From the maps in Figure 1, we observe that Galileo has the highest error, followed by GPS, and then the dual-constellation solution. The map also serves as a method to spatially visualize the tails of the error distribution.
NED Time Histories. We compare the time history of the dual-constellation, GPS and Galileo position solution error to the three sigma (3σ) covariance bound computed at each epoch (see FIGURE 2). We also compare the GPS vs. Galileo inter-constellation difference to the 3σ covariance bound. The covariance bound is never crossed, indicating that 3σ threshold is conservative for both the error and the inter-constellation difference between GPS and Galileo.
FIGURE 2a. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Colorado Springs. (Image: Authors)
FIGURE 2b. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at CU Boulder. (Image: Authors)
FIGURE 2c. Dual-constellation north-east-down error vs. known truth, bounded by a 3σ threshold, at Stanford. (Image: Authors)
Pseudorange Residuals and χ2 Statistic Threshold. Pseudorange residuals have a long history of being used as a consistency check between range measurements. As an example, the pseudorange residuals for the GPS position solutions are shown in FIGURE 3, and their corresponding χ2 statistic is shown in FIGURE 4.
FIGURE 3a. GPS pseudorange residuals at Colorado Springs. (Image: Authors)
FIGURE 3b. GPS pseudorange residuals at CU Boulder. (Image: Authors)
FIGURE 3c. GPS pseudorange residuals at Stanford. (Image: Authors)
FIGURE 4a. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Colorado Springs. (Image: Authors)
FIGURE 4b. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at CU Boulder. (Image: Authors)
FIGURE 4c. GPS χ2 and probability of false alert (PFA) threshold for the nominal noise environments at Stanford. (Image: Authors)
The χ2 statistic is computed using the finite pseudorange residuals at each epoch, where the degrees of freedom are n − 4, where n is the number of satellites used at that epoch and 4 is the number of variables solved for (x, y, z, and the receiver time offset) when using a single constellation. A p-value is computed using the cumulative distribution function (CDF) of the χ2 statistic, and indicates the probability that the χ2 statistic at each epoch would be greater than the observed value. The statistic is compared to a theoretical 10−9 probability of false alert (PFA) based on the theoretical χ2 and the actual degrees of freedom of each epoch. Very low values for the χ2 statistic, such as those obtained with Galileo, are attributed to regions where very few satellites are in view, thus decreasing the degrees of freedom. Any spikes in the pseudorange residuals are also reflected with a higher χ2 statistic and low p-value, though those residuals are de-weighted in the position solution and ultimately do not trigger the 10−9PFA threshold or the 3σ threshold, thus indicating that a 10−9PFA is a conservative threshold.
Power Monitoring. For each nominal location with a u-blox receiver, we analyze results from the power-monitoring metrics mentioned earlier. We also observe results from the internal u-blox jamming indicators in a region where a possible RFI event was observed.
For power monitoring, we analyze spectral power and programmable gain amplifier (PGA) results.
For the nominal noise environments, the spectral power, PGA and corresponding C/N0 results indicated no significant anomalies.
Threshold and Metric Validation Results
An examination of thresholds and other metrics are important for characterizing RFI.
GPS Testing for Critical Infrastructure. From a DHS-sanctioned RFI testing event, we identify five regions of interference or spoofing. To identify the interference, we use a combination of the power and positioning metrics as well as the thresholds we developed through the characterization of the nominal noise environments described in the previous sections of this article.
We use the thresholds and tests we’ve developed to identify regions of spoofing and RFI events (labeled C I1–C I5) in the GET-CI dataset. For ease of comparison, all regions are labeled on plots that display the full 5.5 hours of data collection. All details as to the truth location and time of the test have been removed. C I1 is identified through the power metrics. C I2–C I5 are identified as regions that the NED difference between GPS and Galileo clearly crossed the 3σ threshold in all three directions, as visualized in FIGURE 5.
FIGURE 5a. Map view of solutions using GPS, Galileo and GPS plus Galileo for the DHS-sanctioned RFI testing event (identifying coordinates and physical features removed). (Image: Authors)
FIGURE 5b. Corresponding log-scale visualization of the GPS vs. Galileo position solution difference in the north-east-down directions. (Image: Authors)
From our pseudorange residuals, it appears as though the most significant interference events happened on the GPS constellation, as indicated by the high pseudorange residuals that fall into the C I2 and C I5 regions. Using the GPS χ2 statistic and p-value computations, we determined that the regions that crossed the 10−9 PFA threshold line are consistent with the regions of interference identified in Figure 5. The Galileo χ2 statistic, p-values and pseudorange residuals all show signs of possible interference. These regions are explored more in the power monitoring discussion below.
Since the GPS pseudorange residuals and χ2 statistic results show more signs of spoofing than the Galileo ones, we explore the Galileo-only position solution. Because the truth position is unknown, we take a point during the non-C I regions and define this as the “truth,” that is, a point in the position solution we believe has not been subject to spoofing. Any references to a truth position are from a position recognized as “truth” through post-processing rather than from a pre-determined and known location.
The p-values dip in each of the C I regions, but are lowest in regions C I5. Combined with the fact that the pseudorange residuals and NED error are the highest in C I5, we identify this as the region that likely experienced a significant spoofing event. We determined from an outlier at the beginning of the C I5 region (see Figure 5) that even the Galileo constellation is not immune to the spoofing in this scenario.
To further check the accuracy of our determination that GPS was spoofed, we evaluated the histograms of the Galileo error. With the biggest outlier in C I5 removed, we saw that the error appears relatively Gaussian, with some outliers and possible multi-modal behavior that were also seen in the nominal locations. The variance was higher than was observed at nominal locations, which could be attributed both to the presence of known RFI events, the fact that the nominal noise environment at the RFI event test has not been characterized (that is, it is possible there is a noisier nominal environment at this location), and that the “truth” position was not a known truth but obtained through post-processing of a dataset with increased RFI. Normalized error indicates that the error does not cross the 3σ threshold in any NED direction, further supporting the assertion that 3σ is a conservative threshold.
Important to note is that the major outlier around T+3.5 hours is visible in the NED plot (Figure 5), but the corresponding histograms do not contain that outlier. This indicates that the covariance also increases at that point. It dictates a need to monitor the covariance bound itself, as well as the positioning error. The NED time history plot and the raw error histograms serve this purpose, since it is clear that if we were to be only looking at the error normalized by 3σ, we would not have found significant evidence of the outlier, since the normalized error barely passes the 3σ threshold. This further supports our methods of combining multiple metrics, thresholds and visualizations rather than relying on a single metric to identify jamming and spoofing.
From the Galileo solution analysis, we increase our confidence that we have identified the regions with interference. We removed those areas and looked at the GPS vs. Galileo inter-constellation consistency difference. The normalized differences were now mostly within the 3σ threshold, and the raw error displayed some Gaussian behavior and is no longer on the order of the 105-meter error we were seeing in Figure 5. While these regions still have a higher error than nominal conditions and thus still display signs of interference, we are able to use our spoofing analysis to identify epochs in which we should not trust the GNSS. Using times outside those regions, we are able to figure out a reasonable truth position within 20 meters rather than 200 kilometers.
Positioning analysis using the inter-constellation consistency check is a powerful tool for determining the reliability of a position solution, even when the truth location is unknown. With the power metrics, we can further corroborate the positioning results, as well as find events indicating interference that the positioning metrics were unable to track.
FIGURE 6a. GPS pseudo range residuals for position solutions computed using only the GPS constellation. (Image: Authors)
FIGURE 6b. Galileo pseudorange residuals for position solutions computed using only the Galileo constellation for the DHS-sanctioned RFI testing event. (Image: Authors)
Next Steps and Summary
Leveraging the raw data collected by u-blox receivers in multiple locations with different nominal noise environments, we have developed the toolsets to do inter- and intra-constellation consistency checks to monitor for jamming and spoofing. Many further observables usable for RFI detection are being recorded by the u-blox receivers. Several power monitoring metrics have been evaluated in a preliminary analysis. The next step is to further characterize metrics such as C/N0, AGC and u-blox internal jamming metrics under nominal conditions.
In summary, the tools we have developed so far show that the u-blox receiver will allow for many different consistency checks on a variety of parameters to be running simultaneously. It would be difficult for a spoofer to interfere with all the dimensions we have covered in our detector. Continuously monitoring a wide variety of parameters will increase the chance that we are able to detect interference, thus lowering the chance that a spoofer is able to evade detection.
Acknowledgments
We gratefully acknowledge the support of both the FAA Satellite Navigation Team and The Aerospace Corporation under their university partnership program. We especially wish to thank Steve Lewis of Aerospace for his support and guidance throughout the development of this project. This article is based on the paper “Low Cost RFI Monitor for Continuous Observation and Characterization of Localized Interference Sources” presented at ION ITM 2022, the 2022 International Technical Meeting of the Institute of Navigation, Jan. 25–27, 2022.
LEILA TALEGHANI recently graduated with her MS degree from Stanford University in aeronautics and astronautics and is now a navigation engineer at Trimble.
FABIAN ROTHMAIER is a navigation research and development engineer at Airbus Defence and Space in Munich, Germany, and a former a Ph.D. student at the Stanford GPS Laboratory.
YU-HSUAN CHEN is a research associate at the Stanford GPS Laboratory.
SHERMAN LO is a senior research engineer at the Stanford GPS Laboratory.
TODD WALTER is a research professor in the Department of Aeronautics and Astronautics at Stanford University.
DENNIS AKOS is a professor with the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.
BENON GRANITE GATTIS is a laboratory assistant and undergraduate student in the Aerospace Engineering Sciences Department at the University of Colorado, Boulder.
Better Performance Combining Fiber Optics and Wideband Radio
Innovation Insights with Richard Langley
“OH DEAR! OH DEAR! I SHALL BE LATE!” That’s what the White Rabbit said in the opening chapter of Lewis Carroll’s Alice’s Adventures in Wonderland just before checking the time on its pocket watch. Scientists at the European Organization for Nuclear Research (known by its French acronym CERN) named their project to develop an Ethernet-based network for general purpose data transfer and sub-nanosecond accuracy time transfer after the time-conscious rabbit. CERN’s White Rabbit (WR) can provide sub-nanosecond accuracy to synchronize more than 1,000 nodes via optical fiber or copper connections of up to 100s of kilometers in length. It is a flexible system with a scalable and modular infrastructure with a simple configuration and low maintenance requirements. It is also open source.
WR uses the IEEE 1588 Precision Time Protocol (PTP) to establish precise phase differences between a master reference clock and a local clock. A two-way exchange of PTP synchronization messages allows precise adjustment of clock phase and offset.
So, what has this got to do with GPS or more generally GNSS? Well, for one thing, a WR-based system can serve as a back-up for GNSS time transfer or even replace GNSS. For example, a multi-hop WR link has been installed to connect financial trading locations in Chicago and New Jersey over an approximately 1,350-kilometer distance. Stock markets and other financial institutions need to time-tag transactions with traceable synchronization to a high-accuracy time standard to the microsecond level or better and a WR link can easily provide that.
Another application of WR is in terrestrial positioning. As we know, one of the problems with GNSS positioning is its poorer performance in built-up areas compared to open ones due to blocked signals and multipath. Multipath signals from close-by reflectors can be particularly pernicious as they reduce pseudorange measurement accuracy and thereby increase position error. And another potential weakness of GNSS is its susceptibility to radio-frequency interference, jamming and spoofing. A positioning system using synchronized roadside radio transmitters could be a viable alternative to GNSS in urban areas. A team of researchers based in The Netherlands has developed just such a system. In this month’s column, they describe their system, which uses WR to synchronize the transmissions of wideband radio ranging signals, and how they are able to achieve decimeter-level position accuracy in multipath environments.
By Cherif Diouf, Han Dun, Gerard Janssen, Erik Dierikx, Jeroen Koelemeij and Christian Tiberius
GPS is undoubtedly the most popular system providing positioning, navigation and timing (PNT) services to a host of applications, industries and infrastructures. GPS is mass-adopted, has worldwide coverage, has an impressive up-time and can be used with a wide range of receiver devices, featuring low to high cost and low to high precision.
Despite its strengths, the system also has some weaknesses. For instance, the positioning performance provided by GPS in dense multipath environments, such as in urban canyons, is poor. This is due to the interaction between the desired line-of-sight (LOS) component and close-in multipath components of the GPS signal reflected or scattered by built-up surroundings. Moreover, GPS signals, due to their low received power levels, are fairly vulnerable to unintentional and intentional threats such as radio-frequency interference, jamming and spoofing.
Alternative solutions that may complement or back up GPS, and more generally any other GNSS, to achieve reliable PNT for critical services and infrastructure, such as first responders, telecommunication and power systems, are urgently sought after. Wide coverage area synchronization using White Rabbit optical fiber networks allows simultaneous Ethernet networking and dissemination of 100 picosecond-level accurate time and frequency signals over distances of hundreds of kilometers. Accurate time synchronization may be provided to large areas such as big cities through this technology.
Building on such an accurate system, we present a concept and demonstration of an innovative hybrid optical-wireless terrestrial networked positioning system (TNPS). The TNPS demonstrator uses a White Rabbit infrastructure to accurately synchronize the transmissions of wideband radio positioning signals by its ground-based transmitters (pseudolites) and achieves decimeter-level positioning accuracy in an urban road-like configuration.
SCALABLE FIBER NETWORK DISTRIBUTION
Initially developed for the Large Hadron Collider at the European Organization for Nuclear Research (CERN), White Rabbit (WR) is an accurate and scalable fiber-optic time and frequency transfer method that allows for dissemination of time references at sub-nanosecond level over distances of hundreds of kilometers. A typical WR network layout is shown in FIGURE 1.
FIGURE 1. Simplified topology of a White Rabbit (WR) network for optical time and frequency distribution. The yellow lines represent fiber-optic connections and the blue lines are electrical connections.
A central atomic clock provides synchronization references to a principal WR master switch dubbed the “grandmaster.” The grandmaster feeds synchronization signals into the network. which is expanded via fiber-connected WR devices: the switches and nodes of the network. These devices are serially linked to each other following a hierarchical master-slave pair configuration.
Accurate synchronization between a master and a slave WR-pair is performed as follows. The pair, which is connected via a bidirectional 1.25 gigabits per second optical Ethernet data link, quasi-continuously measures the round-trip delay of the data signals exchanged between the two devices. From these round-trip measurements, the one-way propagation delay, assumed symmetrical, is derived and compensated by the WR devices through an electronic control loop. To take into account possible asymmetries within a link, a calibration procedure is needed when initially installing the connection between a master and a slave. In practice, within smaller scale networks, the synchronization offset accuracy between devices is at the 100 picosecond-level. A 400-picosecond offset between WR devices has even been demonstrated over a distance of 169 kilometers, and more recently over a distance of 800 kilometers.
Besides the fiber-optic connection with other elements of the WR network, each switch or node can share its time-frequency references to an external device or system. These time-frequency references are available either in the form of IEEE 1588 Precision Time Protocol time stamps (via Ethernet connection), or in the form of electrical 1 pulse per second / 10 MHz synchronization signals (via coaxial cables).
THE CONCEPT
Centimeter- or even decimeter-level positioning accuracy is challenging to achieve using GNSS. In dense multipath environments, such as in urban canyons or indoor locations, the accuracy provided by GNSS is poor compared to the meter-level accuracy achievable in open terrain with the Standard Positioning Service. Moreover, GNSS services are vulnerable to interference, spoofing and jamming, and may be denied in indoor areas. We propose a TNPS based on a WR synchronization infrastructure as a complement to GNSS, providing higher timing and positioning accuracy, which also works in challenging environments.
TNPS can achieve decimeter-level accuracy in challenging environments through the use of wideband radio positioning signals. The attainable ranging precision is inversely proportional to the signal bandwidth. Furthermore, in dense multipath environments such as urban canyons, using wider bandwidth signals allows for finer time resolution. As a consequence, close-in received multipath components (MPCs) can be better resolved, and the LOS component can be more easily discriminated from delayed MPCs. This results in more accurate position solutions.
TNPS DEMONSTRATOR
We performed a demonstration of the concept in Delft, The Netherlands, at The Green Village (TGV), an experimental facility on the campus of the Delft University of Technology. The facility aims to accelerate development and implementation of innovations for a sustainable future (see FIGURE 2).
FIGURE 2. Implementation of the TNPS demonstrator. The time-frequency reference is provided by VSL and forwarded to TU Delft via optical fiber (in yellow) and distributed through the optical WR synchronization infrastructure. Wireless radio transmitters (green squares) connected to the WR network deliver wideband ranging signals to perform terrestrial positioning and navigation.
The central synchronization reference of the TNPS demonstrator is the Dutch national timescale version of Coordinated Universal Time UTC(VSL), derived from atomic clocks at the Van Swinden Laboratory (VSL), the Dutch metrology institute. The UTC(VSL) 10 MHz frequency reference and 1 pulse per second time reference are fed to the WR grandmaster switch (WR-SW1). The grandmaster switch is subsequently connected to a distant WR switch (WR-SW2) through a 1,470-nanometer downstream and a 1,490-nanometer upstream 1.25 gigabit per second optical link. WR-SW2, located at one of the TU Delft data centers, synchronizes in turn a WR node (WR-N1) installed at TGV.
The remaining TNPS nodes at TGV are synchronized through a daisy-chain configuration. The first node (WR-N1) is connected to a second one (WR-N2), which is then connected to a third (WR-N3) and so on. In total, five timing nodes, WR-N1 to WR-N5, are connected to one another, using 50-meter optical fibers. These 5 timing nodes are used for synchronization (see FIGURE 3), and provide 1 pulse per second and 10 MHz electrical signals to five wideband radio transmitting units, uTX-1 to uTX-5, installed along a 50-meter stretch of road at TGV.
FIGURE 3. WR timing node (top) fed by a 1.25 gigabit per second bitstream through an optical fiber (yellow cable to the right) and providing electrical 1 pulse per second and 10 MHz synchronization signals at the outputs (two cables to the left). The bottom image shows an SDR system. The two channels of this device, capable of wideband operation, act here as a wireless transmitter or as a receiver. The transmitters are synchronized to the WR network through the 1 pulse per second and the 10 MHz electrical signals (blue-yellow cables at bottom) provided by the WR timing node.
A transmitting unit is based on a wideband transceiver: a software-defined radio (SDR) system linked to a wideband antenna that can operate from 700 MHz to 6 GHz. The antennas are connected to the SDRs using coaxial cables with a length of 5 meters and mounted on lampposts along the road at a height of about 4 meters. The transmitting units, uTX-1 to uTX-5, are respectively associated with antennas TX-1 to TX-5.
Each of these SDRs is capable of transmitting a wireless signal of up to 160 MHz bandwidth, on one or two of the device transmitter channels. The central frequency of each channel is tunable from 10 MHz to 6 GHz. In the demonstrator, we used a 3.96 GHz carrier frequency. The transmitting units periodically stream 160 MHz wideband quadrature phase-shift keying (QPSK) modulated pseudorandom noise (PRN) ranging signals sampled at 200 MHz. The five transmitters operate according to a time-division-multiplexing (TDM) scheme; uTX-1 to uTX-5 successively transmit the QPSK-modulated sequences as a 27.5-microsecond “burst” before turning idle. Between two successive transmissions, a guard interval of 3 microseconds is inserted during which all transmitting units are in idle state. It takes in total 150 microseconds for the five units to complete a transmission round, after which the units remain idle. The transmission round is then retriggered each millisecond.
At the receiver side (RX), another SDR platform is configured to acquire the QPSK modulated bursts transmitted by the five units uTX-1 to uTX-5. This SDR is actually playing the role of a data acquisition platform, which records and forwards the incoming sampled ranging sequences to the host PC via an Ethernet link. All processing and analysis in the demonstrator is performed offline, rather than in real time, using the collected ranging signals. The sampling rate of the acquisition platform is 200 MHz. A sample consists of a 16-bit in-phase value and a 16-bit quadrature-phase value (4 bytes in memory). In continuous operation, the SDR acquisition throughput would amount to 800 megabytes per second.
A throughput of 800 megabytes per second is difficult to handle for most of the host PCs. The SDR is therefore configured to only forward the relevant part of the data collected. Only the received samples time-aligned with the 150-microsecond transmitting window are periodically transferred to the host PC at a rate of 1 kHz. In practice, the acquisition window is slightly extended to 160 microseconds. Overall, the data throughput between the SDR and the host PC is now reduced to 128 megabytes per second; that is, 10 seconds of acquisition will generate a data file of 1.28 gigabytes.
A Schmidl & Cox synchronization sequence is embedded in the signal transmitted by uTX-1. The SDR field-programmable gate array continuously performs autocorrelation on the incoming samples and uses this sequence to detect the arrival time of the ranging bursts for operation in asynchronous mode. The receiver also can be operated in synchronous mode, that is, synchronized to a timing node.
TEST SETUP
We carried out experiments on a 50-meter-long and 6-meter-wide local road at The Green Village (see FIGURE 4).
FIGURE 4. Test road at The Green Village, with three of the five roadside transmitting antennas (TX-3 to TX-5) as indicated. In the foreground, the receiver antenna is mounted on a trolley.
The road is bordered by built-up objects such as brick-wall houses, metal containers and large wooden advertising panels. These generate MPCs, which degrade the radio-positioning performance. The antennas TX-3, TX-4 and TX-5 can be seen mounted on lampposts. In Figure 4, the antennas TX-1 and TX-2 are on the right-hand sidewalk but not visible. The receiving antenna is in front to the left, mounted on a trolley. The RX antenna is identical to the ones used by the transmitters.
The receiver is used to perform a static survey at 50 locations on the road (staying at each point for around 1 minute). As shown in FIGURE 5, the receiver was also used for a kinematic experiment. The RX antenna is mounted on the roof of a car using a wooden beam. The RX antenna is linked via a 3-meter coaxial cable to the receiving SDR placed inside the car and connected to a host PC.
FIGURE 5. Receiver antenna mounted on the roof of a car. Two 360° prisms are used to determine the receiver ground-truth positions at the millimeter level, by means of land surveying total stations (placed on the yellow tripods in the distance).
FIGURE 6 presents a map of the road with the static locations (in blue) and the forth-and-back kinematic track (in red). The transmitting antenna positions are indicated at both sides of the road.
FIGURE 6. Setup of the experiment on the local road at TGV. The locations of the transmit antennas, TX-1 to TX-5, are shown. Locations of static surveyed points are in blue, and the track of the kinematic experiment in red, with the RX antenna mounted on the roof of a car.
To establish a local coordinate system, the ground-truth positions of the RX antenna are determined using two land surveying total stations that rely on retro-reflective targets and 360° prisms to measure distances and angles. In Figure 4, a retro-reflective target, placed directly under the RX antenna, is visible, while the two total stations can be seen halfway down the road on the righthand side. In Figure 5, 360° prisms can be seen on both ends of the wooden beam on the roof of the car. The received signals are used to compute position solutions in post-processing, which are compared to the ground-truth values to assess the positioning accuracy. The accuracy of the ground-truth measurements is at the millimeter-level.
EXPERIMENTAL RESULTS
Achieving high positioning accuracy in a built-up area is difficult due to the presence of close-in MPCs, which arrive with very short time delays following the LOS component. FIGURE 7 shows the observed channel impulse responses (CIRs) between TX-1, TX-3, TX-5 and the receiver antenna RX placed at location 7 (see Figure 6). The LOS components can be easily detected as they correspond to the first and highest peak of each curve. However, we can also observe substantial close-in multipath components, which trail the main peaks. CIRs are obtained by division, in the frequency domain of the fast Fourier transform (FFT) of the received ranging sequences using the FFT of the known transmitted sequence. Oversampling by a factor of 100 is applied, as well as removing time delays of the TDM-scheme, such that the observed time delay difference directly represents the differences in ranges.
FIGURE 7. Normalized magnitude of the CIRs observed between the transmit antennas TX-1, TX-3 and TX-5 and the receiver antenna RX, positioned at reference point 7.
FIGURE 8. Time series of the positioning error in the east and north directions during the kinematic experiment.
Since the TNPS signal bandwidth is 160 MHz, the time resolution is about 6.25 nanoseconds, which corresponds to about 1.9 meters of propagation distance. A multipath component, which arrives at the RX antenna with a lag larger than 6.25 nanoseconds with respect to the LOS component, is likely to be resolved and will not affect (bias) the ranging result. In this case, using time-of-flight techniques, ranges between TX and RX antennas can be determined, often at decimeter level, by extracting the time-of-arrival of the LOS components. Comparatively, if a 20 megasamples per second rate is used (corresponding to a 20 MHz bandwidth, commonly used in GNSS), the time resolution is 50 nanoseconds. An LOS component and a multipath component arriving at the RX antenna can likely be discriminated if the receiver and the reflector are separated by at least 15 meters. If the arrival time between the two components is less than 50 nanoseconds, then the MPC cannot be resolved and will cause a bias when determining the propagation distance between the TX and RX antenna.
The first and largest peak of each CIR seen in FIGURE 7 represents the LOS component. MPCs can be seen trailing the LOS, typically within the next 50 nanoseconds. The MPCs cause a bias in the estimated range when they cannot be resolved. Using wideband ranging signals allows for better time resolution, and better discrimination between the LOS component and the MPCs.
In the following, we assess the 2D positioning accuracy obtained using the demonstrator. 3D positioning is also possible with the demonstrator; however, since the TX antennas all are installed at similar heights and at a fairly low elevation compared to the RX antenna, we restrict our analysis to 2D positioning for the reason of having a poor geometry for determining the vertical position component. The 2D positioning model uses time difference of arrival (TDOA) pseudoranges allowing the cancellation of the asynchronous receiver clock offset. The frequency offset between the transmitters and the receiver has been estimated to be about 1 kHz, in asynchronous mode. After the TDOA ranges are computed from the experimental data, the 2D positioning problem is solved through Gauss-Newton iteration. Statistics of the static and kinematic 2D experiments are presented in TABLE 1.
TABLE 1. Static and kinematic positioning performance in terms of mean, standard deviation (std) and RMSE of position error, in east and north directions.
In the table, we present the mean position errors, standard deviations and root-mean-square errors (RMSEs) over the 50 surveyed points for the east and north directions. Mean errors of 6.4 and 4.3 centimeters are obtained for east and north directions, respectively. There is no significant bias in the system. In terms of RMSE, we can see that the positioning accuracy is just above 10 centimeters (11.6 and 15 centimeters respectively for the east and north directions). Overall, even with the presence of MPCs, and thanks to the synchronization accuracy and the wideband radio signals, a decimeter-level accuracy is achieved in static positioning.
The duration of the kinematic experiment was 84 seconds. Looking at the statistics of the kinematic experiment, the results for the east and north directions show a small bias and RMSE values of 9.2 and 16.4 centimeters respectively. The positioning performance in static and kinematic mode is close, both for the east and north components. In both cases, positioning performance is better in the east direction than in the north direction. This may be explained by better spatial diversity of the antennas towards the east direction. The time series of the position errors for the kinematic experiment is presented in FIGURE 8. Overall, the track error in the eastern direction is within ±2 decimeters (at a 95% confidence level). For the northern direction, a larger deviation is observed in the observation time span from 40 to 50 seconds (forward track), where the error in the north direction is close to 4 decimeters. Such a deviation is likely due to close-in MPCs resulting in a degradation of the accuracy for that part of the track. As a consequence, 82% of the position error in this track lies within ±2 decimeters. Outside this time span, the performance in east and north directions is similar.
CONCLUSIONS
This article presents the concept and results of a demonstration of a TNPS that uses WR to synchronize the transmissions of wideband radio ranging signals to achieve decimeter-level position accuracy in multipath environments, such as in built-up areas. A proof of concept of the TNPS was implemented at TU Delft. The developed prototype system demonstrates a decimeter-level 2D positioning accuracy in an urban road-like configuration bordered by built-up surroundings that cause substantial multipath.
ACKNOWLEDGMENTS
The research described in this article is supported by the Dutch Research Council, Nederlandse Organisatie voor Wetenschappelijk Onderzoek. We thank Lolke Boonstra and Terence Theijn from TU-Delft ICT-FM, as well as Rob Smets of SURF, the collaborative organization for ICT for Dutch education and research for their support and expertise on the optical infrastructure, and Loek Colussi and Frank van Osselen of Agentschap Telecom and René Tamboer and Tim Jonathan of The Green Village for their support in realizing the SuperGPS demonstrator. We also thank project partners Koninklijke PTT Nederland (KPN), Optical Positioning Navigation and Timing (OPNT) and Fugro.
MANUFACTURERS
The WR timing nodes V1.15 are by OPNT. The SDR systems for the transmitters and receiver are National Instruments (Ettus) X310 Universal Software Radio Peripherals. The 3-dBi wide-band antennas CM.02.03 are from Taoglas.
CHERIF DIOUF was a postdoctoral researcher in the Department of Geoscience and Remote Sensing at Delft University of Technology (TU Delft).
HAN DUN was a Ph.D. student in the Department of Geoscience and Remote Sensing at TU Delft.
GERARD JANSSEN is an associate professor in the Circuits and Systems Group of the Microelectronics Department at TU Delft.
ERIK DIERIKX is the principal scientist at Electricity & Time at the national metrology institute VSL in Delft.
JEROEN KOELEMEIJ is an assistant professor in the Department of Physics and Astronomy at the Vrije Universiteit Amsterdam.
CHRISTIAN TIBERIUS is an associate professor in the Department of Geoscience and Remote Sensing at TU Delft.
