Spirent Communications plc is offering a solution that enables the civil aviation industry to evaluate the growing threat of GNSS interference, jamming and spoofing.
The new GSS200D Interference Detector was developed as part of Spirent’s partnership with Nottingham Scientific Limited.
Spirent’s GSS200D interference detector.
As skies and airports become more congested, there is increasing pressure on airports to be safely accessible at all times — which cannot be achieved by relying solely on non-precision approaches with high minimums or on today’s expensive and rigid ground-based infrastructure such as ILS (Instrument Landing Systems).
Ground-Based Augmentation System (GBAS) and instrument approach procedures based on Satellite Based Augmentation Systems (SBAS), such as Localizer Performance with Vertical Guidance (LPV) and Required Navigation Performance (RNP), provide Air Traffic Management with flexible, cost-effective alternatives while providing equivalent operational performance.
For example, the European Geostationary Navigation Overlay Service (EGNOS) launched the LPV-200 service in Europe that enables aircraft approaches without the need for visual contact with the ground until a height of only 200ft. above the runway.
With this service, accessibility, sustainability, efficiency and safety of the landing are greatly improved, especially in bad weather conditions.
Spirent’s new GSS200D solution monitors the radio bands used by EGNOS, as well as other GNSS augmentation systems such as the Wide Area Augmentation System (WAAS) or the GPS Aided Geo Augmented Navigation system (GAGAN), to ensure awareness of interference that could compromise positioning information.
Since local interference near the runway in the GNSS bands could degrade position accuracy or lead to a total loss of the navigation service, it is critical to continuously monitor and understand the RF environment and level of interference around airports.
The GSS200D collects quantitative data on interference allowing assessment of the risks, so that robust mitigation plans can be created. The new Spirent solution has been trialed at a number of European airports, and has collected numerous interference signatures from both unintentional man-made interference and intentional jamming.
“As more airports begin to use GNSS-based instrument approach procedures, they need to know what could be affecting their GNSS signals,” said Martin Foulger, general manager of Spirent’s positioning business. “With this latest solution we can detect interference in the key radio bands, based on levels defined by the United Nations International Civil Aviation Organization and European Organisation for Civil Aviation Equipment. This enables the aviation industry to gain a much better understanding of the electronic environment, helping to avoid dangerous situations going forward.”
For more information on Spirent’s GNSS testing solutions, visit the website. To learn how to test receivers of GPS, Galileo and other GNSS, download Spirent’s latest eBook.
Texas company Continental Electronics has patented a transmitter system and method for construction of low-frequency antenna towers significantly lower in height than previously needed for identical coverage.
“One obstacle to deploying eLoran systems has been the sheer height needed for the transmission towers, each of which requires significant acreage,” said Mike Rosso, vice president of Dallas-based Continental Electronics. “Tower height and land required not only represent serious financial costs, but in some cases adequate space is simply not available. Our technology can reduce tower height and real-estate requirements. With this, reducing antenna tower height by half would reduce required land area to one quarter.”
The method uses digital adaptive correction, solid-state amplifiers, envelope modulation and a wideband matching network. Any linear distortions within usable bandwidth are removed by digital adaptive correction, according to the company. Envelope modulation is required to achieve linearization for any signal type including Loran. A wideband matching network tunes out capacitive reactance from electrically short antennas, transforms impedance to a value suitable for the transmitter, increases usable bandwidth and suppresses harmonics and out-of-band emissions.
“We hope this will aid moving forward eLoran deployments around the world,” Rosso added. “Widely used satellite-based navigation and timing services are vulnerable to jamming, spoofing and other forms of interference. The world needs a more resilient solution as afforded by ground-based solutions such as eLoran.”
You’ve probably heard of at least one of those terms in any discussion around GPS anti-jam technology for defense.
Because they are all terms that describe essentially the same thing: a specialized antenna that helps protect GPS receivers from interference and jamming.
But what exactly are they? Where did they come from? How do they work? What comes next? Read on and find out.
A bit of history
Let’s go back to the Cold War era, at a time when Soviet and Western states were continuously battling for electronic warfare (EW) superiority. In the early to mid-Cold War, radar jamming was the name of the game. Soviet aircraft, such as the TU-16 Badger and its derivatives, carried a range of EW equipment, including some very high-power jammers designed to interfere with radar systems.
Figure 1: TU-16 Badger, an important Soviet electronic warfare platform during the Cold War (Photo: Wikipedia)
Fast forward to the latter years of the Cold War, and we reach the era when the U.S. was busy developing the exciting new GPS system. The Department of Defense (DoD) wanted to ensure that a robust and accurate global navigation system was available to the military, and so the Navigation System with Timing and Ranging (NAVSTAR) launched its first satellite in 1978, eventually becoming the fully operational GPS system by 1993.
Magnificent and ground-breaking though it was, it was recognized very early on that GPS relied on very low-power satellite transmissions, and would be vulnerable if someone tried to interfere with it. Given the prevalence of high-power jamming during the still-ongoing Cold War, there was concern that, if an adversary knew about GPS, they could easily render it useless in a given operational area.
And so it was that the CRPA came to the rescue.
Enter the CRPA
Once again, this GPS anti-jam technology finds its roots in the Cold War, and specifically in radar technology, where engineers developed clever ways to ensure their radars could continue to operate in the presence of jamming. Sidelobe cancellation (SLC) was a well-established technique in the radar community, where a received jamming signal could be “cancelled” by combining the outputs of more than one antenna in the right way.
So, it didn’t take long to adapt this radar anti-jam technology to the problem of GPS protection, and the CRPA was born. At this point I must declare a modicum of national pride, as the earliest operational GPS anti-jam unit that I know of was British. The Plessey PA 9800 GPS Anti Jam Unit was built at Roke Manor in 1984, and tested in the U.S. at the Yuma Proving Ground, Arizona, in 1985.
This pioneering technology could defeat up to three simultaneous jammers in the shown configuration, but was modular in construction, allowing further channels to be added for handling higher numbers of jammers. And all of this in 1984, in the UK, for a U.S. military navigation system that wasn’t even fully operational yet. Incredible.
From then until the present day, CRPAs have seen continual interest and development as the technology of choice to protect GPS from jamming. So how do they work?
Theory of operation
A CRPA is attractive, because it doesn’t require you to make any changes to the GPS receiver itself: It simply replaces the existing antenna. CRPAs are generally larger than typical GPS antennas, because they contain a number of antenna elements, and some associated electronics to do the clever stuff.
There’s nothing magical or mystical about the basics of CRPAs: It’s just standard theory from your favorite textbook on adaptive signal processing. But, as ever, the devil is in the detail — how to make them work well in practice is more involved. And as the technology is generally export-controlled, I shall leave out the important in-depth details.
CRPAs work by exploiting spatial diversity; that is, making use of the fact that the desired satellite signals, and the unwanted jamming signals, generally arrive from different directions. In simple terms, you create a spatial filter, one that removes signals that arrive from particular directions, whilst letting through signals from other directions. To achieve this, rather than use a single antenna, we use an array of antenna elements.
Let’s think in simple and intuitive terms about how this works. Take a look at Figure 3. Here we have a primary antenna P, and some auxiliary antennas A1, A2, and so on. A signal arriving from the direction shown impinges on antenna A2, and slightly later it arrives at A1, and later still it arrives at P. For the sake of argument, if the signal is a simple sine wave, you will then find that the output from each antenna is that same sine wave, but with a different phase shift depending on the spatial arrangement of the antennas.
Now, let’s consider what we call the “weights,” which are labeled as w1, w2 and so on. Each of the weights, in this case, is simply a phase shift that we can define. By careful choice of weights, we could choose to make each of the antenna outputs align perfectly in phase, and then, when we sum all the outputs together as shown, we end up with a bigger version of the input signal.
This is what we would like to achieve if the signal was a satellite. We “steer” maximum overall antenna gain towards that satellite. This is typically what is meant when we refer to “beamforming;” It means steering maximum antenna gain towards a satellite.
Conversely, we could also choose the weights to have the opposite effect: to minimize or completely cancel out the signal. This, of course, is what we would like to do if the signal was a jammer, and is referred to as “nulling” or “null-steering.”
Figure 3. Adaptive antenna basics.How do we determine what those weights should be? Well, this is where your standard theory in adaptive signal processing comes in. Let’s say the objective is to minimize the jamming power out of the antenna. We can write the output power of the adaptive antenna as:
Figure: Michael Jones
The average output power can be found by taking expectations:
Figure: Michael Jones
Taking the minimum and rearranging this leads to the well-known Wiener equation:
Figure: Michael Jones
This Wiener equation is the one to remember. It says that the optimum weights can be found by taking the inverse of the data covariance matrix, and multiplying it by the vector of cross correlations between the primary and auxiliary antennas. As in any adaptive signal processing problem, a simple way to solve the Weiner equation and get the weights might be to use your favorite gradient descent algorithm, such as least mean squares (LMS):
Figure: Michael Jones
However, a solution using this approach does have its problems, for reasons beyond the scope of this article. The mathematics of beamforming are also bit more involved, so I’ll leave that out here.
Rather than the grossly simplified diagram used here, most decent CRPAs also use a more complex architecture based on space-time adaptive processing (STAP) or space-frequency adaptive processing (SFAP). This generally allows much higher levels of jammer cancellation against a wider range of threats.
To finish off this whirlwind section on CRPA basics, let’s see what some example antenna gain patterns might look like. In the figures below, the blue line represents the direction of arrival of a GNSS satellite signal, whilst the red lines indicate the direction of arrival of a jammer. In the first diagram we have a single jamming signal: the antenna gain pattern is a nice hemisphere, as we would generally like, but there is a nice deep null in the direction of the jammer. Moving on to the next diagram, we can see the effect of having three simultaneous jammers on the same CRPA: again we have nice deep nulls in the direction of each jammer, but we are starting to lose more of the sky, and we may start to lose the odd satellite as a consequence. Finally, we have an example of beamforming on a single satellite, whilst nulling out a jamming source.
Again, it’s beyond the scope of this article, but the layout of the antenna elements plays an enormously important part in the performance and behavior of the CRPA.
Figure 4. Illustrative beam patterns of a CRPA antenna in the presence of jamming. (Figure: Michael Jones)Figure 4: Illustrative beam patterns of a CRPA antenna in the presence of jamming (Figure: Michael Jones)
Operational Anti-Jam Units
With some images courtesy of my friends at Raytheon, let’s look at a few examples of deployed military CRPA hardware over the years.
The GAS-1 system entered service in the U.S. in 1997, as a replacement for the earlier AE-1 (1990 to 1996). The CRPA is composed of two parts: the antenna array, which is a seven-element layout, and the antenna electronics as a separate box. The GAS-1 was incredibly successful and became the de facto standard anti-jam technology, fitted to air and sea platforms around the world. Even today, 20 years after its launch, it continues to be fitted to many platforms.
Figure 5. GAS-1 CRPA. (Photo: Raytheon)
By the late 1990s and early 2000s, the Navigation Warfare (NAVWAR) program was in full swing, and the military was looking for enhanced protection against evolving jamming threats. The U.S. initiated a program called Advanced Digital Antenna Production (ADAP). The ADAP product, launched in 2006, was a direct form-fit replacement for the analog GAS-1 system, and introduced a number of advanced features. Most notably, the ADAP simultaneously protects both the L1 and L2 frequency bands, and utilizes STAP processing to achieve high levels of wideband jammer cancellation.
Figure 6. ADAP Digital CRPA. (Photo: Raytheon)
In parallel with the ADAP development, the Digital Antenna Control Unit (DACU) was different in a number of ways. Firstly, it was a true beamforming solution, allowing simultaneous antenna beams to be steered toward satellites, whilst simultaneously nulling out jammers.
Secondly, it was tightly integrated with the GPS receiver, with the GPS receiver hardware located in the same unit.
Thirdly, the DACU was able to perform a number of other advanced functions, such as direction-finding of interference sources. Interestingly, the DACU was used to help locate the source of the interference at the notorious Newark airport jamming incident in 2009.
By the mid-2000s, CRPA electronics were pretty mature and well-understood. The electronics had been miniaturized, and pretty much everything was put onto a single chip. But the physical size of the antennas persisted as a problem for some platforms requiring low size, weight and power (SWAP).
The Landshield, launched in 2014, was a step-change in CRPA technology. Not just because it was a small and fully self-contained unit (about the size of a hockey puck), but because it was the world’s first CRPA to include true anti-spoofing capability.
Figure 8. Landshield Advanced CRPA with Anti-Spoof Technology. (Photo: Raytheon)
Blurring the lines between military and civilian
Going back a few years, the military was heavily focused on CRPAs and anti-jam techniques in general. Military GPS receivers had been developed and deployed, and the question was how they could retrofit robustness to them. At the same time, the commercial world was heavily focused on mass-market GPS receivers — reducing cost, increasing performance — with little care about jamming.
If you’d talked to me five or six years ago, I would have said the military sector is 20 years ahead of the commercial sector in anti-jam technology, and the commercial sector is 20 years ahead of the military sector in receiver technology.
This assertion holds far less true these days; the lines of separation are much more blurred. The military is learning from the commercial world, embracing COTS, and developing new GNSS receivers. Conversely, civilian applications are now much more concerned with jamming, leading to the adoption of low-cost CRPAs in non-military applications.
The future of the CRPA
Where will CRPA technology go from here? We’ve already seen that the latest generation of CRPAs now performs anti-spoofing, as well as anti-jamming. But there is plenty more to see yet.
Although the core technology behind CRPAs is now mature, the trend for the future will be about “doing more with less.” CRPA technology will become more of a multi-function system. Military platforms need to cut down on the number of separate systems they install, and so CRPAs are likely to become multi-functional, performing situational awareness and signals intelligence.
As antenna technology progresses, we will likely see protected navigation solutions utilizing the same hardware as communication systems and radar systems, providing CESM and RESM functions, and being part of an integrated electronic warfare suite. And conformal antennas will see a resurgence of interest for complex and space-constrained platforms.
Raytheon Company and the U.S. Air Force validated performance of an upgraded navigation system for the Miniature Air Launched Decoy-Jammer (MALD-J) in six flight tests from B-52 and F-16 aircraft at White Sands Missile Range, New Mexico.
The system upgrade, designated as GAINS II (GPS-Aided Inertial Navigation System), includes an enhanced multi-element GPS-controlled antenna assembly. The new technology improves MALD-J navigation performance in a GPS jamming environment. Improvements and efficiencies within the design helped to reduce GAINS II unit costs.
“Improving performance while reducing costs is a win for Raytheon and our customer,” said Brian Burton, director of MALD Programs for Raytheon.
Raytheon Space and Airborne Systems in El Segundo, California, supported design work for GAINS II, while Raytheon Missile Systems in Tucson, Arizona, supplied systems engineering, integration and testing. Raytheon is now producing and delivering MALD-J systems with the upgraded navigation.
About MALD and MALD-J
MALD is a state-of-the-art, low-cost expendable flight vehicle that is modular, air-launched and programmable. It weighs fewer than 300 pounds and has a range of approximately 500 nautical miles. MALD-J adds radar-jamming capability to the basic MALD platform.
MALD confuses enemy air defenses by duplicating friendly aircraft flight profiles and radar signatures.
MALD-J maintains all capabilities of MALD and adds jamming capabilities.
Friday is the deadline for GPS manufacturers to apply to test their equipment at a special event with live-sky test scenarios focused on spoofed GPS signals.
The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) is offering an opportunity for manufacturers of GPS equipment used in critical infrastructure to test their products against GPS jamming and spoofing.
The GPS Testing for Critical Infrastructure (GET-CI) event, set for April 17-21 at the Muscatatuck Urban Training Center in Butlerville, Indiana, is the first in a series of test opportunities.
“Accurate and precise position, navigation, and timing (PNT) information is vital to the nation’s critical infrastructure,” said Robert Griffin, acting DHS under secretary for Science and Technology. “S&T has established this program to assess GPS vulnerabilities, advance research and development, and to enhance outreach and engagement with industry. The objective is to improve the security and resiliency of critical infrastructure.”
The GET-CI events provide industry an opportunity to test GPS equipment in unique live-sky environments. For the April event, DHS S&T will be creating live-sky test scenarios focused on spoofed GPS signals.
DHS S&T invites manufacturers of commercial GPS receivers and equipment used in critical infrastructure to submit applications for participation. For submission instructions and further information, see the Request for Information for Participation (RFIP) announcement on FedBizOpps.
Interested organizations should submit their applications for participation by March 3.
Email [email protected] with questions about the event and how to participate.
Spectrum of the Adaptive Notch Filter output signal for various interference levels Photo: Adaptive Notch Filter
Limits of narrowband interference mitigation using adaptive notch filters
By J. Wendel, Frank M. Schubert, Airbus DS GmbH, and A. Rügamer and S. Taschke, Fraunhofer IIS. Presented at ION GNSS+, September 2016.
The robustness of a GNSS receiver against interferences can be increased significantly by using an adaptive notch filter, which estimates the instantaneous frequency of the interfering signal and suppresses it. In this paper, the foundations of adaptive notch filtering are described. Then, experiments are performed with an arbitrary waveform generator for jamming signal generation combined with a space segment simulator for GNSS signal generation. The resulting signals are recorded and post-processed in a software GNSS receiver, which implements an adaptive notch filter for interference mitigation. This setup is used to demonstrate mechanisms that limit the interference mitigation capabilities of adaptive notch filters.
Spoofing, jamming and multipath interference classification using a maximum-likelihood multi-tap multipath estimator
By Jason N. Gross, West Virginia University and Todd E. Humphreys, University of Texas at Austin. Presented at ION ITM, January 2017.
This paper experimentally evaluates the application of existing multipath mitigation technology in conjunction with in-band power monitoring for the purpose of GNSS interference classification. Interference detection and classification metrics derived from the output of a multiple-correlation tap, maximum-likelihood multipath estimator are jointly used for the alarming the presence of GNSS spoofing, jamming or multipath. This approach is evaluated against a dozen sets of deep urban multipath recordings, several recordings of wideband jammers at several different power levels, and clean static data recordings. Two detection approaches are proposed, and one is shown to be better at discriminating between spoofing and jamming attacks.
A diversely polarized antenna array combines signal processing in the spatial and polarization domains for significant improvement in receiver robustness against interference.The C/N0 of line-of-sight components is improved since the receiver can use the power present in the left-hand circularly polarized channels, and also interference mitigation improves.
By Matteo Sgammini, Stefano Caizzone, Achim Hornbostel and Michael Meurer
INNOVATION INSIGHTS with Richard Langley
POLARIZATION. We use the word in everyday speech to mean a division into two groups with sharply contrasting opinions or beliefs.
But the word has another use in physics and electrical engineering to describe a characteristic of electromagnetic waves. Electromagnetic waves, whether they be light waves or radio waves, have electric and magnetic fields vibrating perpendicularly to each other and to the direction of propagation. If the electric field (and, correspondingly, the magnetic field) vibrates in a specific non-changing plane, we say that the wave is linearly polarized.
In terrestrial radio communications, signals are typically transmitted as linearly polarized waves with the electrical field oscillating in the vertical plane or the horizontal plane. Receiving antennas are designed and oriented to preferentially respond to the particular polarization of the signals. Before the widespread use of cable and satellite distribution platforms, VHF and UHF TV signals were received using rooftop antennas consisting of multiple parallel metal rods (similar antennas are used now for terrestrial digital TV).
In North America, the rods were in the horizontal plane since the transmitted signals were horizontally polarized. In Europe, on the other hand, the rods were sometimes in the vertical direction since there, some TV signals were transmitted with vertical polarization.
If the plane of vibration of the electric and magnetic fields rotates uniformly as the signal propagates, we have the case of circular polarization. Since the sense of rotation can be clockwise or anti-clockwise, we have right-hand circularly polarized (RHCP) and left-hand circularly polarized (LHCP) signals following the direction of curl of the fingers of the right and left hands. Circular polarization is typically used for signals from satellites in low and medium Earth orbit, such as GNSS satellites, where the relative orientation of the transmitting and receiving antennas is not fixed. For maximum signal reception, the polarization of the receiving antenna should match the polarization of the signal. All GNSS satellites transmit RHCP signals and therefore most GNSS receiving antennas are designed for such signals.
However, a funny thing can happen to a satellite signal on the way to a receiving antenna. If the signal bounces off a nearby structure or the ground or the sea surface, its polarization is modified and it will become LHCP or a combination of the two polarizations. While this multipath phenomenon can be a pest, as discussed in last month’s column, it can be used to advantage in measuring sea-surface roughness, for example, by monitoring reflected GNSS signals from a low Earth orbiting satellite or an aircraft using a LHCP antenna.
But GNSS receiving antennas are not perfect—especially for direct line-of-sight low-elevation-angle signals. A primarily LHCP antenna can capture a significant portion of the energy in such a RHCP signal and could provide a strong response to a reflected signal when the line-of-sight signal is missing or very weak. So, there could be a benefit in having a dual-polarized antenna to improve positioning capability in marginal situations. Furthermore, jamming signals can be of arbitrary polarization and a dual-polarized antenna array with beamforming capability could better separate and mitigate such interference. In this month’s column, we examine the principles of operation of such an antenna array and how one performed in real-world jamming and non-jamming scenarios.
The rapid growth of the wireless telecommunication sector and, consequently, the high demand of spectrum assigned to the new services make the frequency spectrum very crowded and quite saturated. With the weak received signal power of GNSS signals, spurious harmonics from other systems can cause unintentional interference and, therefore, a serious problem to the reliable estimation of user position, velocity and time (PVT). Besides unintentional interference, more virulent intentionally radiated signals, called jammers, may knock out the GNSS receiver; this is especially the case when a jammer with high time-frequency dynamics (such as a chirp-like jammer) affects the GNSS signal spectrum.
Whether unintentional or intentional, interference represents a serious threat to GNSS in applications ranging from safety-of-life to critical sectors like law enforcement, transportation, communication and finance. In such critical applications, it is important that the GNSS receiver provides a minimum level of reliability and robustness, even at the cost of increased price and complexity.
To meet this need, some manufacturers and research institutions have been developing GNSS receivers equipped with anti-jamming capabilities.
In this article, we propose a novel approach to interference mitigation. We equipped a GNSS receiver with a diversely polarized antenna array to combine signal processing in the spatial and polarization domains in a novel way. By doing this, we demonstrated achievable improvement in interference mitigation. For this purpose, we extended an existing two-step blind adaptive beamforming algorithm to a new algorithm that includes the polarization domain. We evaluated the new algorithm through measurement data gathered during a campaign carried out at the Automotive Testing Center in Aldenhoven, near Jülich, Germany. We used different interference sources, including low-cost jammers, euphemistically called personal privacy devices (PPDs), in real-life situations such as in a moving car approaching a GNSS receiver.
The receiving antenna used in our work is a four-element rectangular dual-polarized (DP) array in a two-by-two configuration. Each element has two feeds available, one ideally receiving the right-hand circularly polarized (RHCP) field and the other the left-hand circularly polarized (LHCP) field of the polarized incoming signals. Due to antenna imperfections and coupling effects, part of the LHCP field impinging on the antenna will be received by the RHCP port and vice versa. Generally, the antenna axial ratio is fine-tuned at boresight so that the energy of a RHCP satellite signal impinging on the array at high-elevation angles will be mostly captured by the RHCP port, while the energy flowing through the LHCP channel can be ignored. This statement does not hold for satellite signals coming from lower elevation angles or in general for signals with polarization other than RHCP, this being generally the case for multipath and interference. In particular, the response of a planar antenna array for angles-of-arrival (AoAs) close to the horizon is almost linearly polarized. It follows that a significant portion of the RHCP energy in a signal is likely to be captured by the LHCP channels and can be used either to strengthen the line-of-sight (LOS) component, or to better separate and mitigate both multipath and interfering (jamming) signals.
Adding the polarization domain makes it possible to better discriminate spatially and temporally correlated signals. In some environments, such as urban canyons, the LOS signal might not be available or might be strongly attenuated. In this case, the reflected non-LOS signals can be used to perform positioning and would benefit from a DP antenna approach. As a matter of fact, the reflected signals will be no longer RHCP, thus the LHCP channel can be used to strengthen the echoes and improve positioning. Diversely polarized antenna arrays also have the advantage of increasing the total number of available degrees of freedom. The number of degrees of freedom of an antenna array corresponds to the number of nulls that can be placed in the direction of arrival of interfering signals. For a single-polarization (SP) array with M elements, M-1 nulls can be placed in the spatial domain. In the case of a DP array, 2M-2 nulls can be placed in the space and/or polarization domains. This is a key factor in counteracting high-power and highly-dynamic jammers, such as PPDs. Furthermore, the use of a diversely polarized array improves signal detection, as well as direction of arrival and polarization-estimation performance. This is particularly true for closely spaced signals with sufficiently separated polarizations. On the other hand, the introduction of the second polarization increases the computational complexity of signal processing, since the number of elements is doubled.
The results of our measurement campaign show a significant improvement in receiver robustness against interference when the DP approach is used compared to the general SP case.
SIGNAL MODEL
In this section, we will briefly describe the theory of signal and antenna polarization with a minimal number of equations. A more complete discussion is included in the paper on which this article is based (see Further Reading).
Polarization of a Plane Wave. A received electromagnetic signal is assumed to be narrow band, and the source of radiation is assumed to be located in the far field. The plane wave propagating in free-space has the property that the direction of propagation is orthogonal to the electric and magnetic field vectors. This allows the electric field e of a polarized wave to be completely described in terms of the two unit vectors, and , orthogonal to the direction of propagation
(1)
where ∈xand ∈y are the real-valued, non-negative, amplitudes of the components of the electric field, Φxand Φyare the phase components of the field, ω is the angular frequency of the carrier and k is the wave number.
Only the real part of Equation (1) is physically relevant, with the complex exponential containing information about the phase of the oscillating field.
Switching from the linear to the circular basis vector set:
(2)
where and are the unit vectors of the RHCP and LHCP components, respectively and omitting the explicit time and spatial dependence, we can write the normalized electric field as
(3)
The polarization state of an electromagnetic signal is fully described by ∈R and ∈L.
More generally, the electric field of any plane wave impinging at the antenna can be expressed in the form
(4)
Dual-Polarized Antenna Array. A circular DP antenna features two orthogonal circular polarization output ports, meaning each element ideally receives the voltage induced by the RHCP and LHCP field components separately on the two different antenna ports. Due to antenna imperfections and the coupling effect, part of the received RHCP field is received by the LHCP port and vice versa. These undesired voltages are responsible for the emergence of the cross-polar components.
In view of this, we characterize the antenna in terms of its response to circularly polarized plane waves and express the electric field using the Jones vector notation in the circular basis as
(5)
where is the complex total electric field received by the RHCP port, is the complex electric field induced at the RHCP port by a purely RHCP electromagnetic wave, indicated as a co-polar component, is the complex cross-polar component of the electric field obtained by exciting the antenna with a purely LHCP electromagnetic wave, φ is the azimuth angle and θ is the elevation angle of the impinging signal assuming the antenna to be at the origin of the spherical coordinate system. Similar statements apply for the total electric field received by the LHCP port, and for the co-polar ( ) and cross-polar () components.
If vR and vL are the complex voltages induced at the RHCP and LHCP antenna outputs by the signal in Equation (4), respectively, the antenna outputs are given by
(6)
where ψ = [θ, φ] is the vector parameter carrying the information about the direction of arrival (DoA) of the incident signal and τ is the time delay of the incident signal.
With an M-element array of DP sensors, we can vR and vL to represent the complex array responses of the DP antenna array:
(7)
where and define the steering vector of the DP antenna array given a signal incident at angle ψ and polarization defined by the Jones vector .
Problem Formulation. The analog signals collected by the antenna array are then passed through the receiver front end where they are amplified, filtered and shifted to baseband. The resulting complex baseband signal with bandwidth B that is received by an antenna array with M DP sensor elements at polarization port P is
(8)
where sp(t) defines the superimposed satellite signal replicas with l = 1 identifying the LOS signal and l = 2, …, L the non-LOS (multipath) signals and zp(t) denotes the superimposed radio frequency interference (RFI) signals with i ranging from 1 to I.
Additionally, we assume temporally and spatially uncorrelated complex white Gaussian noise np(t). can be expressed in terms of the steering vectors given the lth signal’s incident angle, the polarization vector and a complex scalar term involving the signal complex amplitude, Doppler frequency, carrier-phase offset and the particular pseudorandom noise sequence and associated
The baseband signals are then digitized at sampling frequency 1/Ts ≥ 2B. The observations are collected at K periods of the pseudorandom sequence at N time instances and the polarizations of the satellite signals and interferers as well as their DoAs are assumed to be constant over each single observation. We finally combine the two outputs of the DP antenna to benefit from both polarizations with a resulting unified signal output X. This increases the number of available degrees of freedom; furthermore, it allows us to carry out filtering in the polarization domain. On the other hand, the overall system complexity is increased; in particular, the computational complexity of the matrix inversion needed for pre-whitening (to be discussed next) is increased by a factor of about 23.
PRE-WHITENING AND EIGEN-BEAMFORMING
Interference mitigation and beamforming uses a two-step blind beamforming approach based on orthogonal projection. It is similar to an approach we developed for the single-polarization case, with the only difference here being the introduction of the orthogonal LHCP channel, which doubles the number of sensors. Doubling the number of sensors does not necessarily mean that the number of degrees of freedom is also doubled. It has been shown that when using diversely polarized antennas, to discriminate signals unambiguously it is required that the maximum number of signals D = L + I satisfies the relationship D ≥ 2M–2.
This means that one additional degree of freedom is required to discriminate in the polarization domain in comparison to the case of an antenna array of uniformly polarized sensors, where it is required that D ≥ M–1.
Pre-Whitening. We establish a sample spatial-polarimetric covariance matrix, where we assume that the satellite signals, the interfering signals and the noise are uncorrelated among each other. Furthermore, we ignore the influence of the signal replicas, because their power is usually much smaller than the power of the noise and interference. We then obtain the approximate pre-whitening matrix to be applied to X. The pre-whitening matrix is applied before signal despreading.
Eigen-Beamforming. In the next stage, the complex pre-whitened signal passes through the tracking loops for despreading and code and carrier wipe-off. We collect the post-correlation signal at K integration intervals to obtain the data matrix and the post-correlation spatial-polarimetric sample covariance matrix. The post-correlation eigen-beamformer is obtained following the same optimization problem that we solved for the single-polarization case. We apply the optimum weight vector, maximizing the ratio between the power of the desired signal and the power of the undesired signals plus noise, using the eigenvector with respect to the dominant non-zero eigenvalues of the post-correlation covariance matrix.
MEASUREMENT CAMPAIGN
The receiving antenna used in our work is a planar four-element rectangular DP array in a two-by-two configuration, similar to one we have used previously, apart from the additional hybrid couplers needed to provide the LHCP channel outputs. Each element has a double feed, one ideally receiving the RHCP field and the other the LHCP field of the polarized incoming signals, resulting in a total of eight output channels. The single antenna elements are designed for the reception of the GPS L1 and L5 and Galileo E1 and E5 bands, but in this work we focus only on the reception of GPS L1 signals.
The eight signals are passed through a front end, where they are amplified, filtered and down-converted to the intermediate frequency of 2.5 MHz. The analog signals are then digitized with a sampling rate of 8 megasamples per second. The resulting 8-bit digital data are collected and stored on a solid-state drive for data analysis in post-processing. Data analysis is then performed by using a GNSS software-based receiver.
Description of Test Scenarios. The DP system has been tested using measurement data to assess its dual capability of improving the quality of LOS signal reception and robustness against both unintentional RFI and jamming. As mentioned previously, the measurement campaign was conducted at the Aldenhoven Automotive Testing Center. The location provides seven tracks of different lengths, inclinations and shapes. The test track used for this measurement campaign was the so-called autobahn, providing two lanes in each direction of travel and a total length of 1,000 meters (see FIGURE 1).
FIGURE 1. Test track layout.
In this article, we report and analyze the results of three different test scenarios. In the first test, we collected GPS L1 data over 60 seconds in an interference-free environment. The aim of this baseline scenario was to verify if the additional LHCP channels improved signal reception in terms of carrier-to-noise-density ratio (C/N0) and PVT errors.
The second test scenario involved a horn antenna mounted on a mast, transmitting a continuous wave (CW) interference signal in the GPS L1 band and steered in the direction of the receiving antenna, as shown in FIGURE 2. Both the horn antenna and the receiving antenna were kept static during the measurement interval.
FIGURE 2. CW interference scenario.
The objective of the third test scenario was to replicate a real-life situation involving jamming, similar to the so-called “Newark scenario,” where a GPS jammer in a truck driving past Newark Liberty International Airport caused ground-based and satellite-based augmentation systems receivers to malfunction. To carry out this test, we installed a type K-320 PPD jammer transmitting in the GPS L1 band (see FIGURE 3) in the 12-volt auxiliary power outlet (cigarette lighter receptacle) of a moving car approaching the receiver and driving by.
FIGURE 3. The K-320 in-car PPD jammer.
The car started its run at a distance of about 260 meters from the receiver. During the first 20 seconds, the car holds its position. After this time, it was driven in the direction of the receiver with a constant speed of 30 kilometers per hour, finishing its route on the other side of the autobahn track, as depicted in Figure 1.
Baseline Scenario. The benefits that come to light using a DP array are of a dual nature. First, the C/N0 of LOS signals is improved since the receiver can make use of the power present on the LHCP channels due to polarization mismatch, in particular for satellites with low AoA, resulting in better receiver-computed PVT solutions. This effect appears evident if we analyze the behavior of C/N0 values over time collected during the non-interference experimental test in the GPS L1 band.
With reference to the sky plot in FIGURE 4 indicating the positions of the satellites at the time of observation, we analyzed the subgroup composed of those satellites having an elevation AoA lower than 30°. There was a sensible improvement of C/N0 using both polarizations from the DP antenna compared to just using the RHCP output (see FIGURE 5(a)). On the contrary, satellites with an elevation AoA higher than 60° do not benefit from the DP antenna and experienced almost the same C/N0 whether the LHCP channel was used or not, as can be seen in FIGURE 5(b).
FIGURE 4. Receiver sky plot for GPS L1 on October 22, 2015, at 13:10:00 UTC.FIGURE 5. C/N0 improvement using the dual-polarized antenna: (a) low-elevation-angle satellites (elevation angle <30°), (b) high-elevation-angle satellites (elevation angle >60°).
While the advantage of using the DP array is evident when observing the C/N0 behavior, this achievement does not translate with the same clear evidence when assessing the 2-D horizontal position error. Nevertheless, an improvement of about 6 centimeters in terms of the standard deviation of the 2-D position solution error in the horizontal plane has been obtained using the DP antenna (see TABLE 1). It is reasonable to expect that in a scenario where the availability of satellites is not as high as in our test case, the use of low-elevation angle satellites becomes more important for the accuracy of the PVT solution. In this case, the use of a DP antenna could play a key role in improving positioning accuracy.
Table 1. Interference-free RMS positioning error, in meters, in the horizontal plane over 60 seconds. Note that the data for the single-element result was obtained using just one sensor element of the 2 × 2 array in the same test run from which the array DP and array SP results were obtained.
CW Interference Scenario. The use of a DP array provides the ability to filter signals in the polarization domain, and at the same time we benefit from the additional degrees of freedom available. Thus, interference mitigation becomes more effective than using a SP array, increasing the receiver robustness and enabling tracking and successful PVT solutions in a severe interference scenario. This outcome appears evident analyzing the results of our test, where the linearly polarized CW interference described in TABLE 2 impinged on the array.
Table 2. Direction and calculated interference-to-signal ratio (ISR) for 25 dBm transmit power CW interference signal.
We show the 2-D horizontal position errors from this test in FIGURE 6. The figure highlights the improvement in position accuracy when both RHCP and LHCP channels are jointly used, limiting the root mean square (RMS) error to 2.65 meters, while it increases to 3.88 meters when only the RHCP channels have been used.
FIGURE 6. Horizontal position errors over 60 seconds in the presence of CW interference.
The advantages of using the DP array as assessed above are well summarized in FIGURE 7. The figure shows the history of the C/N0 split into two clusters. The upper cluster is from measurements during the interference-free period while the lower cluster is from measurements during the period the receiver is affected by the interference. In the figure, the improvement in terms of C/N0 is notable when using the DP array, in particular for low-elevation angle satellites, and for those satellites having a DoA close to the DoA of the interference. The latter case, when satellite signals and the interfering signal almost overlap in space, has been fully analyzed in a technical note (see Further Reading).
FIGURE 7. C/N0 history for all tracked GPS L1 satellites placed in order of their elevation AoA collected over 120 seconds: (a) using the single-polarized antenna, (b) using the dual-polarized antenna.
PPD Jammer Scenario. The goal of this test was to compare the overall performance of the DP array to the SP array, as well as to the case when only a single-element antenna was used and with no pre-whitening applied. The K-320 PPD employed in this test scenario poses a serious threat to any commercial receiver in obtaining a valid PVT solution. The spectrogram of the K-320 is shown in FIGURE 8(a), which illustrates that the chirp signal sweeps very rapidly (with a sweep interval of about 40 microseconds) across a frequency range of 15 MHz centered at the L1 carrier frequency, as can be seen in the plot of power spectral density in FIGURE 8(b). The frequency range is much larger than the receiver bandwidth of about 8 MHz (dual-sided). This means that the RFI is seen as pulsed RFI by the receiver.
FIGURE 8. Chirp-like signal generated by the K-320 PPD jammer: (a) spectrogram, (b) spectral density.
An estimate of the jamming behavior during the test in terms of interference-to-signal ratio (ISR) is shown in FIGURE 9. The estimated ISR counts only for the portion of jamming power falling within the receiver bandwidth in baseband after down conversion; it is not an estimate of the ISR at the antenna array. The closer the jammer in the passing car is to the receiver, the higher the PPD power affecting it. The minimum distance between the jammer and the receiver is about 14 meters and is reached at 13:13:45 UTC as indicated in the figure.
FIGURE 9. Estimated interference-to-signal ratio (ISR) of the K-320 PPD jammer.
In FIGURE 10, we can observe the impact of the RFI when the car drives past the receiver by means of the number of tracked satellites, or rather by the number of valid pseudoranges available for PVT computation. When the jammer is close to the receiver, the DP antenna is always better than the SP one. When the RFI is at the minimum distance (about 14 meters) from the receiver, the SP antenna is no longer able to deliver a valid position, while the DP antenna still can.
FIGURE 10. Number of available pseudoranges.
The higher number of valid pseudoranges when using the DP antenna is translated into a better position accuracy. This result can be seen in TABLE 3, which lists the RMS horizontal position error computed during the time the estimated ISR is greater than 25 dB. In the computations, only valid PVT solutions and 2-D positioning errors below 20 meters have been considered.
Table 3. RMS positioning error, in meters, in the horizontal plane computed when ISR > 25 dB.
CONCLUSION
The results of the measurement campaign have shown a significant improvement in positioning accuracy and robustness against interference when the dual-polarization approach is used compared to the general single-polarization case. Position accuracy takes advantage of the better C/N0 for those satellites with an AoA below 30°, which experienced up to 2 dB C/N0 improvement. Although the benefit in PVT accuracy was not remarkable in our testing, this should become more notable in scenarios where a lower number of satellites are visible or when the LOS signals are obstructed, such as in urban environments. Receiver robustness takes advantage of the possibility of filtering in the polarization domain and the additional number of available degrees of freedom, enabling tracking and PVT solution availability in severe interference scenarios. In particular, a valid PVT solution was still available for an ISR of 53 dB using the dual-polarization array, while the single-polarization array was unable to deliver a valid position. While these improvements are noteworthy, they do come with added cost and complexity of the receiving system, since the number of channels to be processed is doubled.
ACKNOWLEDGMENTS
This article is based on the paper “Interference Mitigation Using a Dual-Polarized Antenna in a Real Environment,” presented at ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, held Sept. 12–16, 2016, in Portland, Oregon.
MATTEO SGAMMINI received an M.Eng. degree in electrical engineering in 2005 from the University of Perugia, Italy. He joined the Institute of Communications and Navigation of the German Aerospace Center (DLR), Wessling, Germany, in 2008. He is pursuing a Ph.D. in electrical engineering with research interests in interference mitigation techniques for GNSS.
STEFANO CAIZZONE received an M.Sc. degree in telecommunications engineering and a Ph.D. degree in geoinformation from the University of Rome “Tor Vergata,” Italy, in 2009 and 2015, respectively. Since 2010, he has been with the antenna group of DLR’s Institute of Communications and Navigation, where he is responsible for the development of innovative miniaturized antennas.
ACHIM HORNBOSTEL holds a diploma degree in electrical engineering and a Ph.D. degree from the University of Hannover, Germany. He joined DLR in 1989 and heads a working group on algorithms and user terminals at the Institute of Communications and Navigation.
MICHAEL MEURER received a diploma in electrical engineering and a Ph.D. degree from the University of Kaiserslautern, Germany. Since 2006, he has been with DLR’s Institute of Communications and Navigation, where he is the director of the Department of Navigation and of the Center of Excellence for Satellite Navigation. Since 2013, he has also been a professor of electrical engineering and director of the Institute of Navigation at Rheinisch-Westfälischen Technischen Hochschule (RWTH) Aachen.
FURTHER READING
• Authors’ Conference Paper
“Interference Mitigation using a Dual-Polarized Antenna in a Real Environment” by M. Sgammini, S. Caizzone, A. Iliopoulos, A. Hornbostel and M. Meurer in Proceedings of ION GNSS+ 2016, the 29th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 12–16, 2016, pp. 275–285.
“Detection and Suppression of PPD-Jammers and Spoofers with a GNSS Multi-Antenna Receiver: Experimental Analysis” by A. Hornbostel, M. Cuntz, A. Konovaltsev, G.C. Kappen, C. Hättich, C.A. Mendes da Costa and M. Meurer in Proceedings of ENC 2013, the European Navigation Conference, Vienna, Austria, April 23–25, 2013.
“Blind Adaptive Beamformer Based on Orthogonal Projections for GNSS” by M. Sgammini, F. Antreich, L. Kurz, M. Meurer and T.G. Nollin in Proceedings of ION GNSS 2012, the 25th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 17–21, 2012, pp. 926–935.
“Field Test: Jamming the DLR Adaptive Antenna Receiver” by M. Cuntz, A. Konovaltsev, M. Sgammini, C. Hattich, G. Kappen, M. Meurer, A. Hornbostel and A. Dreher in Proceedings of ION GNSS 2011, the 24th International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, Sept. 19–23, 2011, pp. 384–392.
“Suppression of Multipath and Jamming Signals by Digital Beamforming for GPS/Galileo Applications” by Z. Fu, A. Hornbostel, J. Hammesfahr and A. Konovaltsev in GPS Solutions, Vol. 6, No. 4, March 2003, pp. 257–264, doi: 10.1007/s10291-002-0042-2.
• Diversely Polarized Antenna Arrays
“Subspace Fitting with Diversely Polarized Antenna Arrays” by A.L. Swindlehurst and M. Viberg in IEEE Transactions on Antennas and Propagation, Vol. 41, No.12, Dec. 1993, pp.1687–1694, doi: 10.1109/8.273313.
“Direction Finding with an Array of Antennas Having Diverse Polarizations” in IEEE Transactions on Antennas and Propagation, Vol. 31, No.2, March 1983, pp. 231–236, doi: 10.1109/TAP.1983.1143038.
• Antenna Array Signal Processing
“Two Decades of Array Signal Processing Research: The Parametric Approach” by H. Krim and M. Viberg in IEEE Signal Processing Magazine, Vol. 13, No. 4, July 1996, pp. 67–94, doi: 10.1109/79.526899.
“Multilinear Array Manifold Interpolation” by R.O. Schmidt in IEEE Transactions on Signal Processing, Vol.40, No.4, April 1992, pp. 857–866, doi: 10.1109/78.127958.
Two new topic areas and presentations have been added to this Thursday’s free webinar on Signal Interference: Detection and Mitigation.
The speakers will explore anti-jamming protection with controlled radiation pattern antennas (CRPAs) and with dual-polarized antennas. The latter topic is also the cover story for the February issue, which demonstrated a significant improvement in positioning accuracy and robustness against interference with a dual-polarization approach: a gain in terms of C/N0, particularly for low-elevation angle satellites and valuable in urban environments.
Headshot: Kirk Burnell
Kirk Burnell from NovAtel joins the Feb. 2 panel to present “How to deliver assured positioning, navigation and timing in GNSS-compromised environments.”
He will look at applications that stress the importance of high-reliability PNT. Compromised GNSS signals due to unintentional interference is of great concern, but intentional interference due to jamming is much more insidious. Anti-jamming protection via controlled reception pattern antenna (CRPA) technology is now available to a wide range of users. A brief explanation of the technology will be followed by a few use-cases where CRPAs have been deployed in a variety of applications.
Burnell, Core Cards Product Manager for NovAtel, has worked at the company since 2015. With an education in survey engineering, Kirk has been working with precision GNSS system designers and integrators in both support and product management capacities for more than 20 years.
Headshot: Matteo Sgammini
Matteo Sgammini of the German Aerospace Center (DLR) will talk about work with dual-polarized antennas: the principles of operation of such an antenna array and how one performed in real-world jamming and non-jamming scenarios. This ION GNSS+ 2016 presentation became the cover story for GPS World’s February issue.
Innovation editor Richard Langley writes in his introduction to the February column, “All GNSS satellites transmit RHCP [right-hand circularly polarized] signals and therefore most GNSS receiving antennas are designed for such signals. However, a funny thing can happen to a satellite signal on the way to a receiving antenna. If the signal bounces off a nearby structure or the ground or the sea surface, its polarization is modified and it will become LHCP [left-hand circularly polarized] or a combination of the two polarizations.
“A primarily LHCP antenna can capture a significant portion of the energy in such a RHCP signal and could provide a strong response to a reflected signal when the line-of-sight signal is missing or very weak. So, there could be a benefit in having a dual-polarized antenna to improve positioning capability in marginal situations. Furthermore, jamming signals can be of arbitrary polarization and a dual-polarized antenna array with beamforming capability could better separate and mitigate such interference.”
February cover story. Photo: GNSS
Researchers at the DLR equipped a GNSS receiver with a diversely polarized antenna array to combine signal processing in the spatial and in the polarization domain. Tests show a significant improvement in receiver robustness against interference compared with the general single-polarization case.
The carrier-to-noise-density ratios of the line-of-sight components are improved since the receiver can use the power present on the left-hand circularly polarized channels, particularly for satellites with low elevation. Interference mitigation improves due to the possibility of filtering in the polarization domain and the additional number of available degrees of freedom.
Sgammini received a Masters degree in electrical engineering from the University of Perugia, Italy and now works at the Institute of Communications and Navigation, DLR. He is currently pursuing a Ph.D. in electrical engineering with research interests in interference mitigation techniques for GNSS. His research activity includes adaptive filtering, array signal processing and estimation theory for GNSS.
As the number of GNSS signals being tracked increases, so does the potential for interference to dismiss the performance gains of using those additional signals.
To maximize performance and efficiency, prepared PNT users need their equipment to be able to detect when interference is present and mitigate it.
Developers, integrators and users need mitigation tools to protect and preserve GNSS measurement quality, maintaining high-quality multi-frequency multi-constellation positioning performance, even in challenging RF environments. This is essential particularly on the integration journey, especially during prototyping and when encountering unforeseen interference events in field testing, in order to produce fully successful integrated products.
The one-hour webinar also will include a follow-up Q&A session with the speakers. Burnell and Sgammini join Patrick Casiano of NovAtel and Rick Hamilton of CGSIC on the speaker panel. Casiano will present an Interference Toolkit that measures RF spectrum levels and allows the user to apply mitigation tools to protect and preserve GNSS measurement quality. Hamilton will explain the proliferation of jammers, aspects of illegal use, coordinated government response to interference events, and regulations to prohibit manufacture, import, export, sale and use of jammers.
A strategic alliance announced on Dec. 15 between Orolia and Satelles includes product development and go-to-market activities of positioning, navigation and timing (PNT) solutions provided by the Iridium satellite constellation, independent of GPS/GNSS signals. The companies intend to provide PNT solutions to military, defense, government and commercial customers worldwide.
Orolia, the parent of GNSS-active companies Spectracom and Spectratime, among others, has formed a strategic alliance, including an equity investment, with Satelles Inc. to develop, market and sell PNT solutions based on Satelles’ satellite time and location (STL) signal technology.
STL is a unique space-based PNT technology that provides location and timing data independent from traditional GPS and other GNSS satellite signals. By using STL, Orolia’s Spectracom and McMurdo solutions will, according to the company, be less susceptible to vulnerabilities such as spoofing, interference and jamming that are associated with GPS/GNSS.
Based on the low-Earth orbit (LEO) Iridium satellite constellation, STL signals are up to 1,000 times stronger than GPS/GNSS; this signal strength, due in part to the constellation’s closer proximity to users, helps to prevent jamming and enables signal reach into buildings and other difficult locations. STL’s additional cryptographic security also ensures performance, productivity and security.
For further background on Iridium, see GPS World’s June 2016 Defense PNT column, “Iridium and GPS revisited: A new PNT solution on the horizon?” Projected applications and use cases include energy/utility grids, enterprise data networks including financial systems, maritime/aviation navigation, fleet/asset tracking management, search and rescue, and data center management.
Many highly sensitive military, defense, government and commercial applications and operations require accurate and reliable PNT data. Today, these applications rely on signals from GPS/GNSS satellites. There are instances, however, where GPS/GNSS signal strength and security are not sufficient and prone to signal disruption. For these cases, the companies jointly state, STL can be used as a secure signal of opportunity to complement GPS/GNSS, making the applications more accurate and secure, and less prone to interference and attack.
“There is a growing need for precise and robust positioning, navigation and timing information especially in business-critical, high-risk and life-saving operations,” said Jean-Yves Courtois, Orolia CEO. “By augmenting Orolia’s GPS/GNSS-based solutions with Satelles’ STL technology, we will have the industry’s first essentially fail-safe, resilient PNT solution. This breakthrough offering will be ideal for mission-critical applications in which the smallest discrepancy in PNT data accuracy, availability and stability can produce a network outage, a system crash or a loss of life.”
Signal strength, availability
The technical advantages provided by adding ranging satellites in low-Earth orbit (LEO) to the GNSS satellites in medium-Earth orbit (MEO) were explored in a 2012 Institute of Navigation paper by Per Enge, Bert Ferrell, David Whelan, Greg Gutt and David Lawrence. GPS World plans to publish an updated version of that paper, with key new material on current STL performance statistics, in an upcoming issue.
Briefly, the paper concluded that “Due to their proximity, signals received from LEO are approximately 30 dB stronger than the signals from MEO. Indeed, we show data collected inside an industrial-strength metal storage container. The power of a LEO signal received inside the container is approximately equal to the power of a GPS signal received under the open sky. On the other hand, LEO proximity also dictates that only a few Iridium satellites are in view of the ground-based user. We show typical examples where six to 11 GPS satellites are joined by one or two LEO satellites.”
The authors then examine the effect of the swift mean motion of LEO satellites, analyzing the ability to whiten multipath based on the rapid motion of the line-of-sight vectors from the user to the LEO satellites. In sharp contrast to MEO, the LEO satellites attenuate errors due to multipath solely based on satellite motion, and do not require user motion. They also analyze Doppler-based positioningvusing the rapid mean motion of the LEO satellites. The Doppler shift projects onto the line-of-sight vectors from the user to the LEO satellites. Over 100 or 200 seconds, this projection is a sharp function of the user location, and this connection enables Doppler-based positioning similar to the Transit satellite system. The authors’ analysis shows that position accuracies of 5 meters can be based on noncoherent code tracking of the LEO plus GPS signals.
This paper also discusses the broadcast of UTC time to sites with known locations, describing experimental results with absolute time accuracies of one microsecond. The broadcast of high-accuracy frequency from LEO would enable a high-accuracy hot clock to replace the relatively low-quality oscillator in GNSS receivers, allowing longer coherent and non-coherent averaging times and improving the sensitivity of GNSS receivers by several decibels. Many other navigation applications would benefit from one LEO satellite in view, the authors assert.
Market view from operator’s CEO
“We are a manufacturer and integrator of timing equipment,” Orolia CEO Jean-Yves Courtois told GPS World. Orolia is the parent company of GPS/GNSS product and service providers Spectracom, McMurdo and Spectratime. “This new STL service is not fully commercialized yet, but it’s operational and it can be tested. Receivers are available and can be integrated into our equipment.
“The timing signal is very accurate and close enough to GPS for most timing applications, although the positioning accuracy is lower than what GPS users are accustomed to. It is an augmentation for timing primarily, and secondarily for positioning,” Courtois continued.
“In terms of timing accuracy, it provides on the order of tenths of microseconds in accuracy, and this covers a lot of timing applications. This is an ideal timing backup or augmentation of GPS. In positioning it’s closer to 50 meters or more, much better for fixed objects than for mobile objects. The faster the vehicle, the lower the positioning accuracy. It’s not directly usable for GPS applications that require a few meters’ accuracy, but it can be associated with inertial navigation for much better results.
“The STL signal penetrates buildings well, it has unique features, and it performs at a high level. The signal is encrypted, so you have to subscribe to a service to receive a key, allowing access to the signal. Applications are developing based on equipment that will be STL-enabled. For the user it will be transparent. The user will have a different antenna.
“We are also active in tracking and emergency location devices, where this is also of interest. It has some authentication capability, to guarantee that the person who accesses the signal is in the location that he pretends to be.
“For customers to be able to use this service, there is some integration work to be done, some dedicated STL receivers to integrate into our current hardware set up, and software modifications. We are ready to work with government and defense organizations and other new clients. Our basic interest is to add some robustness to our equipment for our current customers, and then of course to develop new customers worldwide.”
Grab It’n’Go Drive-By Shopping
Four years ago, retail giant Amazon, a leader in the elimination of human interaction, started to explore what shopping would look like if you could walk into a store, grab what you want, and leave. In early December, the company rolled out its new vision: Amazon Go.
Currently in private beta testing in Seattle and scheduled to open to the public in early 2017, the system employs a fusion of sensor technologies including RFID to detect when a shopper takes an item from the shelf, sync the data to the shopper’s handheld device, sense when the shopper leaves the store area, then charge all collected items to the shopper’s Amazon account. No muss, no fuss.
The company is keeping a tight lid on exactly how its system works, but earlier patent filings give some description of the confluence of sensor data.
“In some implementations, data from other input devices may be used to assist in determining the identity of items picked and/or placed in inventory locations. For example, if it is determined that an item is placed into an inventory location, in addition to image analysis, a weight of the item may be determined based on data received from a scale, pressure sensor, load cell, etc., located at the inventory location. … By combining multiple inputs, a higher confidence score can be generated increasing the probability that the identified item matches the item actually picked from the inventory location and/or placed at the inventory location.”
A strategic alliance announced on Dec. 15 between Orolia and Satelles includes product development and go-to-market activities of positioning, navigation and timing (PNT) solutions provided by the Iridium satellite constellation, independent of GPS/GNSS signals. The companies intend to provide PNT solutions to military, defense, government and commercial customers worldwide.
Orolia, the parent of GNSS-active companies Spectracom and Spectratime, among others, announced that it has formed a strategic alliance, including an equity investment with Satelles Inc. to develop, market and sell PNT solutions based on Satelles’ satellite time and location (STL) signal technology. STL is a unique space-based PNT technology that provides location and timing data independent from traditional GPS and other GNSS satellite signals. By using STL, Orolia’s Spectracom and McMurdo solutions will, according to the company, be less susceptible to vulnerabilities such as spoofing, interference and jamming that are associated with GPS/GNSS.
Iridium satellite, courtesy Iridium.
Based on the low-Earth orbit (LEO) Iridium satellite constellation, STL signals are up to 1,000 times stronger than GPS/GNSS; this signal strength, due in part to the constellation’s closer proximity to users, helps to prevent jamming and enables signal reach into buildings and other difficult locations. STL’s additional cryptographic security also ensures performance, productivity and security.
Projected key applications and use cases include energy/utility grids, enterprise data networks including financial systems, maritime/aviation navigation, fleet/asset tracking management, search and rescue and data center management. Further details on planned projects and products of the Orolia-Satelles partnership will be posted to this site in a follow-up story in coming days.
Many highly sensitive military, defense, government and commercial applications and operations require accurate and reliable PNT data. Today, these applications rely on signals from GPS/GNSS satellites. There are instances, however, where GPS/GNSS signal strength and security are not sufficient and prone to signal disruption. For these cases, the companies jointly state, STL can be used as a secure signal of opportunity to complement GPS/GNSS, making the applications more accurate and secure and less prone to interference and attack.
“In today’s increasingly dynamic and mobile world, there is a growing need for precise and robust positioning, navigation and timing information especially in business-critical, high risk and life-saving operations,” said Jean-Yves Courtois, Orolia CEO. “By augmenting Orolia’s market-leading GPS/GNSS-based solutions with Satelles’ STL technology, we will have the industry’s first essentially fail-safe, resilient PNT solution. This breakthrough offering will be ideal for mission critical applications in which the smallest of discrepancies in PNT data accuracy, availability and stability can result in a network outage, a system crash or a loss of life.”
“Satelles’ pioneering role in STL technology is a perfect fit with Orolia’s proven Resilient PNT strategy,” said Michael O’Connor, Satelles CEO. “We look forward to working together to introduce new products and solutions that will provide our customers with the utmost confidence that their positioning, navigation and timing data is accurate, secure and accessible.”
Not with Purple Haze, but with signal interference — although, come to think of it, the two may be not unalike, phenomenologically.
The October reader’s poll asked “Have you directly experienced any of the following? Check all that apply.
GPS/GNSS jamming.
GPS/GNSS spoofing.
Unintentional RF interference.
RF interference from unknown source; unknown whether intentional or not.
None of the above.
Other, please specify.
The answers rather stunned me in their magnitude. To be sure, respondents were self-selected and thus not totally representative of the electorate (you) out there. People who have undergone jamming or spoofing would be much more likely to step forward and say “Yeah, here,” than those who had not would be to fill out an online form, however brief, simply to say “Nah, not me.”
At any rate, the answers came back:
Jamming: 70 percent (70 percent!)
Spoofing: 25 percent
Unintentional RF interference: 55 percent
Unknown RF interference: 65 percent
None of the above: 5 percent
Among the “other” answers we received were these:
I’ve participated in official test activities; Incidents caused by GPS booster (low-cost repeater); We regularly see our vehicle tracking systems jammed or providing incorrect positions believed to be via organised theft using sophisticated jammers; Every time I drive past Newark, NJ on I-95; Badly installed GPS antennas, RF interference from old GPS antennas.
Scanning the affiliations of those answering, the names of organizations actively involved in monitoring or countering jamming and spoofing rise to the top. Still, to get such overwhelming response — only one in 20 was not experienced in this realm — suggests time and energy invested in protections and countermeasures should be doubled, quadrupled or more. Disasters of many kinds loom.
Speaking of disasters, and of our fondness for placing our finger on the pulse of the GNSS/PNT community, we held a mock presidential plebiscite at ION GNSS+ in September. “Who will be the best GPS president?” That is, who would be the best president for GPS, in terms of funding and support? The answers: Clinton 60 percent, Trump 34 percent. The real results may already be known by the time you read this. And, to paraphrase Gerald Ford (something I never thought I’d find myself doing), our long national nightmare may be over.
A: Current integrations of GPS include a controlled reception pattern antenna (CRPA). Testing with a standard interference or jamming source will not provide accurate results. Wavefront generator simulators are capable of outputting signals that correctly stimulate the GPS receiver’s antenna electronics. All of the signals are correctly displaced according to the antenna’s reception pattern with a jamming source that is coherent.
A: Testing GNSS receiver spoofing and jamming resilience under real-life scenarios requires mixing live-sky GNSS signals with synthesized spoofed signals. This requires the spoofing signal generator to be time- and position-locked to the live-sky signal to within nanoseconds. GNSS simulators that allow nanosecond-level synchronization to live-sky signals can enable such testing. Low-cost simulators can enable testing with multiple simultaneous spoofers/jammers.
A: With the sophistication of GNSS threats, simulators should be able to generate a variety of interferences and jammers that users can easily control. Also, the jammers’ characteristics (Doppler, power level, and so on) should reflect the dynamic of the vehicle and jammers. Such characteristics are almost impossible to simulate when the jamming source is not integrated with the simulator.
A: For jamming, test for multi-frequency/constellation, accurately controlling jamming-to-signal ratios and strength levels, and simulate several types of jammers: carrier-wave, sweep, noise, FM chirp and so on. For spoofing, two synchronized simulators are best: one for the live sky and one for the spoofer. Tightly control the sync accuracy, the relative power between the two signals, and the spoofer’s estimation accuracy of the target’s position.
A: Antenna technology, directionality and filtering have a large part to play in mitigating the impact of jamming and spoofing. Conventional laboratory receiver testing often overlooks the effect of the antenna. New approaches need to be developed to allow antenna effects be incorporated into testing either by including the antenna to be part of the test setup or by accurately simulating the directionality/filtering capability of the antenna.
A: Most jamming occurs due to RFI used to keep positioning unavailable. As such, typical jammers are CW or sweep-CW. Testing is then mostly a matter of proper jamming-to-signal simulation. On the contrary, spoofing aims at luring the receiver from its true position. Simulations are difficult as slowly power increasing spoofing signals must be synchronized with true received signals to take over the locked tracking loops.
