Precise timing grandmaster with gateway clock and high-performance boundary clock enhances 5G mobile network phase protection
To help 5G mobile providers, cable operators and utility providers ensure phase delivery, protection and synchronization even when GNSS is offline, jammed or spoofed, Microchip Technology Inc. has released software version 2.1 for its TimeProvider 4100 precision timing grandmaster.
TimeProvider 4100 is a 1588 grandmaster including support for the latest ITU-T G.8275.1 and G.8275.2 1588 phase profiles, complemented by extensive port fan-out for PTP, Network Time Protocol (NTP), SyncE, and E1/T1.
Software release 2.1 builds on earlier versions by adding key software enhancements providing a virtual Primary Reference Time Clock (vPRTC). Virtual PRTC provides the ability to design a redundant precise time distribution architecture for phase protection over an optical network.
Until recently the main source of precise time has been GPS and other constellations that comprise GNSS. Deployment of GNSS, however, can be costly for service providers given the costs associated with upgrading to GNSS-capable receivers and antennae as well as increasing densification of end points.
As a result, telecom, cable and utility operators deploying vPRTC benefit from solutions where GNSS dependency is reduced or eliminated. Following are key features of the new vPRTC functionality:
Leverages the existing optical network, avoiding high-cost dark fiber expenses
Uses a dedicated lambda to transport time precisely and securely
Provides a high-performance, redundant source of time through enhanced PRTC (ITU-T G.8272.1)
Allows bidirectional, precise time flows (east and west)
Chains together high-precision, multi-domain, high-performance boundary clocks that meet today’s standards (T-BC Class D, as defined by ITU-T G.8273.2)
Microchip’s vPRTC multi-domain architecture is a cost-effective solution providing a high-performance, redundant, sub-5 nanosecond distribution of precise time over regional and national networks.
In addition, Release 2.1 introduces Network Time Protocol daemon (NTPd) with Message Digest (MD5) security algorithm.
TimeProvider 4100 2.1 meets PRTC-B performance standards (per ITU-T G.8272) and supports 1G and 10G, NTP and PTP in a single form-factor system. TimeProvider 4100 2.1 is available now for both new and already deployed systems.
IMO is the United Nations body that coordinates and sets standards for international maritime operations and safety.
In a paper dated March 10, the service said that GNSS signals are “essential to safe and efficient navigation and an integral component of all maritime operations.” Interfering with them “jeopardizes the safety of life at sea.”
Deliberate disruptions in the eastern Mediterranean and the Black Sea, the paper says, affect vessels operating in international waters and engaged in innocent passage through territorial seas.
While nations typically have a right to do as they wish in their sovereign territory, they are also obliged to not have that intrude into other nations’ territory or international waters. This is also true for vessels passing through their waters but not calling at their ports, known as “innocent passage.”
The International Law of the Sea Treaty stipulates that, in the absence of some clear wrongdoing such as piracy, drug smuggling or discharging oil, vessels be allowed to pass through territorial seas unmolested by the coastal state.
The Coast Guard paper also points out that nations have other treaty obligations that prohibit this kind of activity. International Telecommunication Union Radio Regulations prohibits “All transmissions with false or misleading identification…”
Citing a March 2019 report in GPS World, the paper also documents that GNSS disruption is a global problem not confined to just one or two areas. A study by the German Aerospace Center (DLR) found interference during every phase of a vessel’s voyage between Europe and the Far East.
The Coast Guard paper was submitted for consideration at IMO’s Maritime Safety Committee that had been scheduled to meet on May 13, but has been postponed due to the COVID-19 emergency.
This planned consideration at IMO follows a resolution by the UN’s International Civil Aviation Organization (ICAO) in May 2019. In a paper entitled “An Urgent Need to Address Harmful Interferences to GNSS,” the International Federation of Air Traffic Controllers’ Association (IFATCA), the International Federation of Air Line Pilots’ Associations (IFALPA), and the International Air Transport Association (IATA) had introduced the issue.
This resulted in a resolution describing the eliminating interference as an urgent need.
About the same time the U.S .Coast Guard paper was due to be considered, IMO was to engage in the early stages of considering rules for autonomous vessels. Its Facilitation Committee was scheduled hold a “Regulatory scoping exercise for the use of Maritime Autonomous Surface Ships (MASS)” at a meeting the end of April. This meeting has also been postponed.
While not specifically mentioned, navigation issues will undoubtedly be part of the considerations when discussion of rules for autonomous shipping eventually takes place.
Public input to these international meetings is always sought in advance. For example, the U.S. State Department had announced a meeting for April 6 to receive public input on U.S. positions for the various issues to be discussed at the Facilitation Committee.
While we understand that this meeting will also be also be postponed, comments can be submitted to the points of contact listed in the Federal Register announcement as well as be raised during the eventual meeting.
J-Shield is a robust filter on Javad GNSS antennas that blocks out-of-band interference (Figure 1). In particular, J-Shield blocks signals that are near the GNSS bands, including the proposed Ligado Networks (formerly LightSquared) broadband signals, explained Javad Ashjaee, founder and CEO of Javad GNSS.
FIGURE 1. Protection characteristics: The J-Shield filters have a sharp 10-dB/KHz skirt, which provides up to 100-dB of protection. (Image: JAVAD GNSS)
The anti-jam digital filters protect against in-band interference such as the harmonics of nearby TV and radio stations, or against illegitimate in-band transmissions. The anti-jam filters can be combined in pairs for complex signal processing and can simultaneously suppress several interference signals.
“The filters make the near band spectrums available for other uses,” Ashjaee said. “They protect GNSS bands now and in the future.”
In-Band Noise Measurement. The receiver measures the level of interference as a percentage of noise above the normal condition. Figure 2 shows the condition in a clean environment, where eight GPS satellites were visible, according to the almanac. In all, eight C/A, six P1, six P2, six L2C and two L5 GPS signals were tracked. The noise level was 2% on C/A and L5 and 0% on P1, P2, and L2C.
FIGURE 2. Clean environment. (Image: JAVAD GNSS)
Figure 3 shows 290% noise in the GPS C/A signal and 121% noise in Galileo E1. Only one of the eight GPS C/A code and none of five Galileo E1 signals could be tracked because of the high level of interference.
FIGURE 3. High interference levels. (Image: JAVAD GNSS)
Spectrum Analyzer
Filters in the GNSS antenna provide one way to protect GNSS signals from interference. Another is the receiver chip itself. For instance, the Javad GNSS Triumph chip includes an integrated spectrum analyzer — a more efficient solution than using a commercial spectrum analyzer to continuously monitor and evaluate the environment, Ashjaee explained.
The spectrum analyzer monitors the spectrum inside the chip. It has an effective bandwidth of 1 KHz, and can be programmed to automatically record the spectrum (and other information) periodically or according to pre-set conditions. Each spectrum shows the power and shape of any interfering signals and jammers.
Figure 4 shows the shape of the GPS L1 band spectrum when the band is jammed, as indicated by the huge peak in the center where the C/A code is. The number on the bottom left is the height of the peak. The height of the spectrum is 21.1 dB; compared to a calm spectrum of 11.2 dB, this spectrum indicates a jamming impact of about 10 dB.
FIGURE 4. The L1 band is jammed, as shown by the peak. (Image: JAVAD GNSS)
Automatic Gain Control. In addition to monitoring the spectrum, the Triumph chip also keeps a record of automatic gain control (AGC) — another indicator of unwanted external signals. The AGC monitors the environment and adjusts the gain to keep the voltage at a certain level. The change in AGC is an indicator of interference.
Spoofers
“Spoofers are quite different from jammers,” Ashjaee said. “They don’t disturb the environment and the spectrum shape. They broadcast a GNSS-like signal to fool the GNSS receivers to calculate wrong positions. We detect spoofers by digital signal processing.”
With 864 channels and about 130,000 fast-acquisition channels in the Triumph 2 chip, it has the resources to assign more than one channel to each satellite to find all of the signals transmitted with the same GNSS PRN code — including spoofed signals.
“If we detect more than one reasonable and consistent correlation peak for any PRN code, we know that we are being spoofed and can identify the spoofer signals,” Ashjaee said. The chip isolates and ignores the wrong peak.
“Usually more than 100 signals are available at any given time. We need only four good signals to compute position,” Ashjaee said. “We reject infected signals, and then among all the available GPS, GLONASS, Galileo, BeiDou, IRNSS and QZSS signals, we use the healthy ones. It is extremely unlikely that we can be spoofed without our knowledge. We can immediately recognize spoofing and take corrective actions. In the rare case that all signals are affected, we inform the user and guide them to use a compass and altimeter to get out of the jammed area.”
Figure 5 is a screenshot from the company’s Triumph-LS survey receiver, showing the details of each signal tracked. The first six lines in this screenshot show the spoofed signals that were detected as soon as they appeared (number “1” in the C1 column). Percentages show the amount of interference above the normal level.
In the last column, T indicates the signal was tracked by the main channels, Q by the fast-acquisition channels, and U indicates the signal was used in position calculations.
Figure 5. Signal Details: The Triumph-LS receiver provides users with a wealth of information on each signal received, including spoofed signals.
Indicators for Healthy Signals
In addition to the spectrum shape and AGC, these other indicators show the health of GNSS signals:
Number of signals tracked.
Divergence of SNR from its expected value.
Level of additional power and its RMS.
Divergence of AGC from its normal value and its RMS.
Extra noise.
Number of signals spoofed.
As an aid to users, the company’s Triumph-LS receiver can display the status of all GNSS signals received. Figure 6 shows this compact view, with normalized values of the above indicators (0 means good and 9 means poor).
Figure 6. Signal Status. Information on all GNSS signals received as shown by the Triumph-LS. (Image: JAVAD GNSS)
Users of the Triumph-LS can click on any of the signal buttons to see the actual and normalized values of the indicators for that signal. Action buttons provide quick access to View Satellites, View Spoofing, View Spectrum and Take Spectrum. Jamming and spoofing protection is an option on all Javad GNSS products and OEM boards.
What is or would be the best policy response from Congress and/or executive branch agencies to the growing threats to GPS from jamming and interference?
Brad Parkinson
“Homeland Security has declared GPS to be an essential system to virtually all of our infrastructure. It is time to install a national system to identify and shut down interference. As part of that, all cell phones should periodically report interference to that national system and allow law enforcement to pinpoint and eliminate offenders.”
-Bradford W. Parkinson
Stanford Center for Position, Navigation and Time
Allison Brown
“On Dec. 5, 2018, the president signed into law the National GPS Timing Resilience and Security Act tasking the Secretary of Transportation with establishing a backup timing system for GPS within two years. To date, only limited technology demonstrations have been performed. Congress needs to fund the Department of Transportation to rapidly acquire and deploy a back-up timing capability, using available commercial solutions, to assure resilience within the Air Traffic Control system and other critical infrastructure to GPS jamming or spoofing.”
-Alison Brown
NAVSYS Corporation
Members of the EAB
Tony Agresta Nearmap
Miguel Amor Hexagon Positioning Intelligence
Thibault Bonnevie SBG Systems
Alison Brown NAVSYS Corporation
Ismael Colomina GeoNumerics
Clem Driscoll C.J. Driscoll & Associates
John Fischer Orolia
Ellen Hall Spirent Federal Systems
Jules McNeff Overlook Systems Technologies, Inc.
Terry Moore University of Nottingham
Bradford W. Parkinson Stanford Center for Position, Navigation and Time
Ships sailing through the Strait of Hormuz and the Persian Gulf have been experiencing GPS interference that U.S. officials suspect is the work of the Iranians, according to CNN.
The U.S. Department of Transportation’s Maritime Administration issued an advisory on Aug. 7 to ships traveling in the Persian Gulf, Strait of Hormuz, Gulf of Oman, Arabian Sea and Red Sea. Ships have reported GPS interference, bridge-to-bridge communications spoofing and jamming, and other problems.
Iran’s goal is for ships and aircraft to wander into Iranian waters or airspace, justifying a seizure, a U.S. defense official told CNN. He said Iran has placed GPS jammers on Iran-controlled Abu Musa Island, which lies in the Persian Gulf close to the entrance of the Strait of Hormuz.
“Heightened military activity and increased political tensions in this region continue to pose serious threats to commercial vessels,” reads the advisory. “Associated with these threats is a potential for miscalculation or misidentification that could lead to aggressive actions. Vessels operating in the Persian Gulf, Strait of Hormuz, and Gulf of Oman may also encounter GPS interference, bridge-to-bridge communications spoofing, and/or other communications jamming with little to no warning.”
In at least two incidents, vessels reported GPS interference. One vessel reportedly shut off its Automatic Identification System (AIS) before it was seized, complicating response efforts.
Vessels have also reported spoofed bridge-to-bridge communications from unknown entities falsely claiming to be U.S. or coalition warships.
Since May 2019, the following maritime incidents have occurred in this region:
Six attacks against commercial vessels.
Shoot-down of U.S. Navy remotely piloted aircraft over international waters
Attempted at-sea interdiction of Isle of Man-flagged M/V British Heritage (oil tanker)
Seizure of ex-Panama-flagged M/V Riah (oil tanker)
Seizure of U.K.-flagged M/V Stena Impero (oil/chemical tanker)
Detention and subsequent release of Liberian-flagged M/V Mesdar (oil tanker).
A report filed with NASA’s Aviation Safety Reporting System and published in June outlines how a passenger aircraft flew off course during a period of GPS jamming and nearly crashed into a mountain. Fortunately, an alert radar controller intervened, and the accident was averted.
Friedman Memorial Airport serves the ski resort town of Sun Valley, Idaho. Mountain peaks in the area are in excess of 12,000 feet. Airport arrival and departure procedures are carefully structured to ensure aircraft maintain safe distances from terrain.
According to the report, when “Aircraft X” arrived there was “…an abundance of smoke in the area” of the safe arrival route. Also “During this time there was widespread GPS jamming… Almost every aircraft was reporting…GPS outages.” Two previous flights had advised that their GPS signals were interrupted, but came back on line in time to make a safe approach to landing.
Aircraft X also reported problems with GPS, and then advised air traffic control that GPS had come back on line and was working well. The controller then cleared the aircraft for a GPS-based approach, including descending to 9,000 feet. Communications with and control of the aircraft was switched from Salt Lake Center (250+ miles away) to the tower at the local airport.
Shortly thereafter, the controller in Salt Lake City noticed Aircraft X straying off course. Also, it was at 10,700 feet altitude and nearing a 10,900 feet mountain. He quickly contacted the local control tower and the aircraft was directed back onto a safe flight path.
The report concludes that “Had [the Radar Controller] not noticed, that flight crew and the passengers would be dead, I have no doubt.”
Dana A. Goward is president of the Resilient Navigation and Timing Foundation.
A new investigative report by the Russian independent media group “The Project” into luxury dachas owned by high-ranking government officials revealed that most all include GNSS jammers among their amenities. Attempts by the journalists to photograph the dachas from the air using drones were routinely foiled by jamming.
Most all nations’ military and security services have equipment that can block GPS and other satellite navigation signals over areas both large and small. Russia, though, has advanced this to a fine art which it regularly demonstrates.
Russian forces always been proud of their electronic warfare capabilities. They see them as an essential counter to the effectiveness of western high-tech weapons. The news outlet “Sputnik” reported in 2015 Russian military claims that their ability in electronic warfare “makes aircraft carriers useless.”
GPS is an underlying technology for many western weapons, and for much of the west’s critical networked infrastructure. As a result, jamming and spoofing GPS and other GNSS has long been a priority for Russian forces.
In 1997 a Russian company offered a handheld four-watt GPS and GLONASS jammer that was effective at ranges of up to 150 to 200 kilometers. They also reported working with the Russian military on directional antennas for this jammer. These antennas would focus the disruption on a particular target while leaving most other users unaffected. The U.S. Army was sufficiently interested that, in 2002, they reportedly spent almost $200,000 to purchase the jammers for testing and evaluations.
In 2016 Russia announced a program to add GPS jammers to more than 250,000 cell towers as a partial defense against a U.S. cruise missile attack.
That same year a Moscow Times headline proclaimed, “Kremlin Eats GPS for Breakfast!” GPS users near the Kremlin had been regularly finding their cell phones reporting that they were 20 kilometers away at an international airport. This was playing havoc with Uber and Lyft drivers, as well as delivery services that depended upon satellite navigation. This spoofing, or sending false information to receivers, was reported to be an effort to protect the Kremlin and leaders from attack and surveillance by drones. Most drones are programmed at the factory with the locations of airports and to fly away from them. Convincing receivers near the Kremlin or elsewhere that they are really near an airport helps keep the area drone-free.
Independent technologists in Moscow also reported that this spoofing employed a classic electronic warfare technique called “herding.” GPS L2 and L5 signals and Russia’s GLONASS satellite navigation signals were jammed. This forced receivers to rely upon the L1 signal which was spoofed.
That same year this same kind of activity was also detected in the Black Sea. The RNT Foundation reported that over 600 ships had been “transported” to airport locations ashore. A subsequent report in 2019 by the non-profit group C4ADS revealed almost 10,000 instances of ships being spoofed in the Black Sea, the Baltic and in Russia’s west near Vladivostok between 2016 and 2018. It also drew a strong correlation between the movements of Russian President Vladimir Putin and the spoofing events.
Russian jamming and spoofing has not been limited to its homeland. Vehicles, ships and aircraft in other nations, as well as in international waters and airspace, have been impacted. This despite Russia’s treaty obligations under the International Telecommunications Union radio regulations which provide that “All transmissions with false or misleading identification are prohibited.”
The C4ADS report documented a massive Russian “smart jammer” operating almost continuously in Syria that had impact far beyond that nation’s borders. Smart jammers, by their definition, transmit messages that seem to be valid GPS signals, but with content that does not allow receivers to calculate a location. The operation in Syria has caused multiple warnings by the U.S. Maritime Administration of GPS disruptions in nearby international waters, and the European air traffic agency issuing warnings for international airspace in the eastern Mediterranean.
The Baltic and Scandinavia have also seen Russian GPS jamming in recent years. In 2017 the Secretary General of NATO complained about Russian naval jamming that also degraded cell phone service in Latvia, Norway and Sweden.
Early this year Norway protested Russian jamming in its far north, some of which was timed for NATO exercises. Five significant jamming events in the previous 17 months impacted, aviation, construction and other users.
Russia regularly demonstrates that GNSS jamming and spoofing can be a useful tool for internal security and an effective method of power projection. Its actions, along with the portability and proliferation of jamming and spoofing equipment, are undoubtedly meant to remind the west that Russia can take away essential GNSS services at any moment with a just the flip of a switch.
Scientists continue to search for new technologies to serve the PNT mission. One novel way to augment GPS comes from a newly developed technology involving a quantum magnetometer.
Researchers at Lockheed Martin call it Dark Ice; it uses magnetic sensing as an alternative means of determining location without use of satellite signals.
Mike DiMario and his team have developed a prototype magnetometer that uses a synthetic diamond the size of a salt crystal to measure the direction and strength of nearly imperceptible magnetic field anomalies. They overlay that data with maps of Earth’s magnetic field, supplied by the National Oceanic and Atmospheric Association, to produce precise location information.
Special quantum-level impurities in the molecular structure of the diamond, where intermittently a carbon atom drops out and its neighbor is a nitrogen atom, enable the detection of magnetic field waves. These nitrogen vacancy (NV) centers are hyper-sensitive magnetic sensors. When illuminated by a laser, the diamond emits more or less light depending on the surrounding magnetic field’s strength.
The Dark Ice quantum magnetometer measures about 31 centimeters in length. (Image: Lockheed Martin)
Position + Direction. Dark Ice differs from current magnetic sensors aboard ships and planes in that it can measure both the field strength and the direction the field is pointing. “The real advantage of this quantum-based technology is its ability to produce a true magnetic field vector, while at the same time having a very large dynamic range and bandwidth,” DiMario explained.
Project development “was like peeling an onion: with each new layer removed, the team advanced. We had no idea of the expected outcome, other than what system modeling, the laws of physics and good engineering could predict. There was always something we could not have predicted or even thought of.”
In addition to developing this navigational capability, the team has also demonstrated that Dark Ice can harness Earth’s magnetic field to transmit communications across barriers intended to block all traditional signals, and track moving vehicles in real time.
Unjammable. “This project was designed for times when extenuating circumstances might prohibit your use of traditional GPS signals, and you need something that is unjammable, passive and always available. The Earth’s magnetic field meets this description if we can adequately sense and make use of it,” DiMario said.
He wants to downsize Dark Ice to hockey-puck size for convenient use on multiple platforms. “In real-world conditions, if I can get within 200 meters of GPS accuracy, that would be a huge success,” he claimed. Such precision would serve as a backup or verification to GPS, not a sole-means navigation system.
With its powerful sensing capabilities and small size, Dark Ice could function as the most reliable way to do things like identify hard-to-find watercraft in search-and-rescue missions and fly aboard aircraft in the battlefield. Navigation, search and communications — all in one compact sensor.
Recent years have seen an increase in drivers turning to cheap GNSS jamming devices in order to move around undetected or to thwart built-in anti-theft systems or road tolling systems. These jammers not only knock out their own GNSS receiver, they also block GNSS signal reception in a radius of several hundred of meters.There is a growing demand for automatic detection of these illegal jammers to help catching the offending driver.
Septentrio GNSS antenna placement on highway gantry. (Photo: Septentrio)
An ION GNSS+ 2018 presentation by Wim de Wilde and Jean-Marie Sleewaegen presentation showed how a multi-antenna GNSS receiver with built-in RF spectrum monitor and adequate processing tool can efficiently detect and classify jamming events and identify the offending car or truck. They conducted a five-day test with two Septentrio AsteRx-U dual-antenna receivers installed on an overhead structure above a busy highway.
In parallel to the GNSS tracking and built-in anti-jam functionality, the AsteRx-U can simultaneously sample the RF signal from its two antennas. One of the objectives of the test was to evaluate the possibility to perform lane detection by cross-correlating the jamming signal received by the two antennas. In addition, the antennas were mounted with a significant inclination angle to create an asymmetrical reception pattern.
The goal was to assess the feasibility of detecting the driving direction from the time series of the received jammer power. Such lane or direction detection would greatly help identifying the offending driver in heavy traffic conditions when more than one vehicle crosses the overhead structure at the time of the jamming.
Over the five days of the experiment, 45 jamming events were recorded and analyzed, most of them intentional: continuous wave, chirp or even less-known pulse jammers.
Chirp jammer example. (Charts: Septentrio)
The researchers explained how the jamming events are automatically detected and classified by the processing tool, using pattern recognition to distinguish between intentional harmful events and unintentional interferences. They presented selected cases illustrating the RF signature of the most prevailing types of jammer.
They then addressed the direction and lane sensing algorithm and discussed the effect of multipath propagation of the jammer signal. All algorithms are illustrated with real-life examples.
Using Wavelets for a Robust Vector-Tracking-Based GPS Software Receiver
Innovation Insights with Richard Langley
ALFRÉD HAAR. Who is he, you might ask? Alfréd Haar was a Hungarian mathematician who introduced the concept of wavelets during his Ph.D. work on orthogonal functional systems under David Hilbert of Hilbert transform fame. And what is a wavelet? Generally speaking, a wavelet, as its name suggests, is a brief oscillation in time with an amplitude that begins at zero, goes through one or more variations, and returns to zero. It’s a bit like the cardiac cycle of each heartbeat shown on an electrocardiogram. But wavelets, unlike heartbeats, are mathematical functions with well-defined properties.
Although Haar initiated the use of wavelets back in 1909, it was not until the 1970s and 1980s that the study of the use of wavelets — wavelet analysis — was undertaken to help solve a variety of problems in science and engineering with new application areas springing up all the time. We’ll get to one of these new areas — GNSS jamming mitigation — in just a bit, but let’s discuss a more mundane application first.
Let’s say we have a digitized audio recording of Maynard Ferguson’s rendition of “MacArthur Park” in our computer. We could do a Fourier transform (related to the Hilbert transform mentioned earlier) of the entire recording, which would show us all of the specific audio frequencies making up the song. But what if we wanted to determine where in the song Ferguson played a particular high note, such as double high C (not his highest)? We could create a wavelet with that frequency and a short duration such as that of a 32nd note and use the mathematical operation of convolution (involving shifting, multiplication and integration) to find one or more spots in the recording with a similar frequency. We could extend the procedure and use a set or bank of wavelets to fully study the song in both frequency and time.
Wavelet analysis will work on many kinds of data, not just audio signals. With an appropriate set of wavelets, we could decompose the data without gaps or overlap, store the resulting product for further analyses and, if necessary, reconstitute the original data with minimal distortion. The U.S. Federal Bureau of Investigation uses wavelet analysis to store compressed digital versions of fingerprint images. A heavily damaged recording of Brahms playing one of his own compositions on an Edison wax cylinder was partially restored using wavelet analysis despite the music being immersed in noise. And the small effect of El Niños on the Earth’s rotation has been studied using wavelet analysis.
And, yes, wavelet analysis is helping to improve the use of GNSS. The tasks being undertaken include de-noising of pseudorange measurements, cycle-slip detection and elimination in carrier-phase measurements, and separating biases such as multipath from high-frequency receiver noise. In this month’s column (which, by the way, now appears four times per year), we learn about another GNSS application of wavelet analysis — specifically the use of the wavelet packet transform — to efficiently identify and separate a jamming signal from the combined signal in a GPS receiver. In a narrowband jamming test using a hardware simulator system, no positioning was possible with conventional receiver operation. But with the proposed approach, the jamming signal was readily suppressed, allowing the satellite signals to be acquired and a positioning solution to be computed. Thank you, Alfréd Haar.
GPS technology has been integrated into many aspects of our daily lives. Hence, there is a growing demand for a robust GPS receiver that can operate efficiently without external aiding to provide continuous, reliable and accurate positioning, navigation and timing (PNT) solutions. However, this is not always possible due to frequent loss or attenuation of signals, multipath or interference. In such challenging conditions, a system malfunction can cause safety problems, especially in health-critical applications.
Receiver architecture plays a major role in defining a receiver’s robustness against the challenges just mentioned. Scalar-tracking-based GPS receivers can achieve high navigation accuracy under line-of-sight (LOS) conditions. However, they always fail to provide adequate accuracy in signal-degraded environments such as urban, suburban and dense foliage environments. On the contrary, vector-tracking-based GPS receivers provide better performance in such challenging environments. In vector-tracking-based receivers, both the tracking loops and the navigation processor are combined to solve a single estimation problem. Hence, there are many advantages of this architecture over that of scalar-tracking-based receivers. First, information from strong signals from healthy satellites is used to track weak signals, when signals are highly attenuated or even totally blocked. Thus, vector-tracking-based receivers have better immunity to jamming and interference. Second, they can rapidly reacquire signals after a satellite outage. Third, they have an improved navigation solution accuracy compared to that of scalar-tracking-based receivers, even under normal LOS conditions. All of these advantages make vector-tracking-based receivers the best platform for our research on receiver robustness. However, vector-tracking-based receivers still suffer from degraded performance in the presence of strong jamming signals. Therefore, we are proposing a new anti-jamming technique to be employed for interference mitigation in vector-tracking-based GPS receivers.
The spread-spectrum nature of GPS signals provides resistance to narrowband interference due to the spreading and despreading processes that take place at the transmitter and receiver respectively. However, a GPS signal reaches the receiver with very low power on the order of –158 dBW, which makes it vulnerable to jamming. A jammer with enough power and suitable time and frequency properties can degrade the positioning solution accuracy and may cause a total blockage of the GPS signals. Besides, the presence of a jamming signal increases the challenge of acquisition of the desired GPS signal.
Therefore, many anti-jamming techniques have been employed for interference mitigation in GPS receivers. There are various anti-jamming methods for GPS receiving systems, which are mainly classified into four groups:
Antenna-level techniques, which are based on the use of antenna arrays to generate a radiation (reception) pattern that attenuates the interference signal based on the direction of arrival.
Automatic gain control (AGC), where interference can be detected by the saturation of the AGC.
Post-correlation techniques, which process the signals after passing through the correlators.
Pre-correlation techniques, which are based on processing the signals after passing through the analog-to-digital converter but before they get to the correlators.
This article introduces a novel interference mitigation technique based on the wavelet packet transform (WPT), which belongs to the pre-correlation techniques category. The WPT enables the received interfered combined GPS signal to be represented in a transformed domain in which an interference signal can be better identified and separated, without significant degradation of the useful GPS signal. The WPT has been extensively discussed in the literature in the framework of GPS and other GNSS. For example, wavelet multi-resolution analysis has been used in one study to remove the multipath error and leave the useful signal untouched. In another study, multi-resolution analysis using wavelets was applied to pseudorange and carrier-phase GPS double differences to reduce multipath effects. And in another, researchers developed a technique to detect and remove cycle slips based on wavelet multiresolution analysis.
The WPT has been widely used in the context of jamming to mitigate pulsed and narrowband interference. Although the WPT showed outstanding performance in jamming mitigation, the main drawback of this technique is the computational complexity. In this article, we introduce a novel wavelet packet-based technique for narrowband jamming mitigation with significantly reduced computational complexity.
Signal and Interference Models
The GPS signal employs a direct sequence spread spectrum communication technique, in which the signal is multiplied by a spreading or pseudorandom noise (PRN) code. As mentioned earlier, this spreading technique gives GPS some immunity to narrowband jamming. The received digitized spread spectrum signal at the output of the receiver’s analog to digital converter (ADC) can be represented by:
(1)
where, for signal s, ym(nTs) is the useful GPS signal received from mth LOS satellite, j(nTs) is the jamming signal, w(nTs) is additive white Gaussian noise (AWGN), M is the number of visible satellites, n is the sample number and Ts is the sampling rate.
The useful received GPS signal can be described as follows:
(2)
where P is the signal power, d(nTs) is the navigation data, c(nTs) is the spreading pseudorandom noise code, fIF is the intermediate frequency, no is the code delay, fd is the Doppler shift, and θo is the carrier phase.
Interference signals are classified based on their spectrum characteristics: narrowband or wideband depending on the signal’s bandwidth relative to the bandwidth of the desired GPS signal.
Our focus in this article is on the mitigation of narrowband interference, specifically a linear chirp signal. A chirp signal can be expressed as:
(3)
where a is the chirp signal amplitude, fo is the starting frequency, k is the sweeping frequency, and Tsw is the sweeping frequency period. The chirp is continuously repeated.
Wavelet Packet Transform
The wavelet packet transform or WPT is a class of transformed domain techniques that has been widely used in the context of jamming mitigation in GPS signal reception. It allows for the study of a signal in both time and frequency domains simultaneously. In the WPT, the signal is decomposed into approximations (the low-pass component) and details (the high-pass component) with respect to a group of local basis functions. These functions can be obtained through dyadic scaling and shifting of the so-called mother wavelet. The discrete wavelet basis functions are given by:
(4)
where j and k are integers, so is the dilation step, and τo is the scaling coefficient. The decomposition of the signal with respect to a scaling function acts as low-pass filtering of the signal, while the decomposition with respect to a wavelet function acts as high-pass filtering of the signal. The signal is then down-sampled, and this procedure is further iterated on all the sub-bands using scaled and dilated versions of the wavelet and scaling functions. This filtering process allows the decomposition of the GPS signal with respect to a local basis function, in which each of these sub-bands identifies a limited frequency band of the received signal, and the frequency resolution is dependent on the level of decomposition. The wavelet packet decomposition can be realized as a filter-bank as depicted in FIGURE 1.
As mentioned earlier, the main drawback of WPT is the time complexity. Due to the decomposition of both approximation and detail components, if the signal is decomposed into L levels, the resultant number of coefficients is 2L. For instance, if we used 10 decomposition levels, the resultant number of wavelet coefficients is 210 (1,024). However, as each wavelet coefficient component represents a limited portion of the frequency of the received signal, the jamming signal will only affect a few coefficients. Thus, the main idea of the proposed algorithm is to identify those coefficients that are affected by the jamming signal and reconstruct the jamming signal after denoising them. Then, the estimated jamming signal is subtracted from the received signal to obtain the jamming-free useful GPS signal.
Identifying the wavelet coefficients affected by interference is achieved by computing the median absolute deviation (MAD). As those coefficients that are affected by interference have a higher MAD value than those that are not affected, the decision of whether the wavelet coefficients are affected by interference is based on comparing their MAD values with a certain predefined threshold. This threshold is determined based on the desired detection and false alarm probabilities according to the distribution of the received signal samples in an interference-free environment. Only the sub-bands whose MAD values exceed the threshold are considered to be affected by interference and are further decomposed.
Therefore, only the sub-bands affected by interference are isolated and iterated. This technique allows for a considerable reduction in complexity, as both detection and mitigation can be applied in a limited number of sub-bands. FIGURES 2 and 3 show the tree decomposition of the received signal of two jamming scenarios based on the proposed algorithm. The frequency offset of the jamming signal from the GPS signal is 200 kHz in the first scenario and 600 kHz in the second one. The figures clearly illustrate the huge reduction in computational complexity as for 10 levels of decomposition; we ended up having only eight wavelet coefficients instead of 1,024.
FIGURE 2. Tree decomposition for scenario I. (Image: Authors)FIGURE 3. Tree decomposition for scenario II. (Image: Authors)
The proposed wavelet packet-based detection and mitigation algorithm is explained in three steps.
Decomposition Step. The incoming GPS signal is decomposed through a uniform filter bank by only one level. Then, MAD is computed for all the decomposed sub-bands. Only sub-bands with a MAD value greater than the predefined threshold will be further decomposed. This step is repeated until the maximum predefined decomposition level is reached.
Detection Step. The MAD value is computed for all sub-bands from the last decomposition level. Only sub-bands whose MAD value exceeds the predefined threshold are considered affected by interference and are used to reconstruct the jamming signal using the inverse wavelet transform.
Reconstruction Step. In this step, the useful GPS signal is reconstructed free of interference by subtracting the estimated jamming signal from the received signal.
Experimental Work
In our investigation, a GNSS simulation system was used to create a fully controlled environment to examine and validate the performance of the proposed method using semi-real simulation scenarios. The simulator was controlled by simulation software that enables the simulation of multipath reflections through an advanced multipath model as well as atmospheric degradation to signals and the effects of antenna patterns and terrain obscuration. Moreover, it can generate simulated land, air, space and sea trajectories. Furthermore, the simulator when connected to an interference simulation system can provide various controlled jamming environments using an interference signal generator. The full setup is shown in FIGURE 4.
The receiver used in this research is a prototype of a digital front end. The front end collects the output radio frequency (RF) signal from the simulator. Then, the RF signal is down-converted to baseband through several down-conversion stages, generating the in-phase (I) and quadrature-phase (Q) data. Then, the data is sampled and quantized through the ADC. The front end collects GPS L1 signals at different bandwidths ranging from 2.5 MHz to 20 MHz with quantization levels ranging from 1-bit to 8-bit. After that, the sampled digitized signal is sent to the computer via an Ethernet connection.
The raw I/Q GPS samples are then processed by a GPS software receiver. Our proposed algorithms have been implemented using Matlab by modifying the open-source GPS software-defined radio (SDR) receiver composed by Borre and Akos, which is widely used in research.
To verify the performance of the new proposed algorithm, a full GPS C/A-code signal was simulated using the previously mentioned simulation system. A static simulated scenario was generated for this purpose. This static scenario was run twice, once in an interference-free environment for reference, and one where the jamming signal was enabled. The simulation, front end and SDR receiver parameters are shown in TABLE 1.
Table 1. Data collection and processing parameters. (Data: Authors)
FIGURES 5 and 6 show the power spectral density (PSD)of the received signal before and after applying the proposed jamming mitigation technique. The figures demonstrate that the interference components have been highly attenuated. To confirm the benefits of the proposed technique, the reconstructed useful GPS signal has been acquired using the SDR receiver.
FIGURE 5. PSD before jamming mitigation. (Image: Authors)FIGURE 6. PSD after jamming mitigation. (Image: Authors)
FIGURE 7 shows that the receiver is in a total blockage as it failed to acquire any satellite before applying the jamming mitigation technique. However, FIGURE 8 shows that the proposed algorithm allowed the retrieval of seven satellites.
FIGURE 7. Acquisition results before jamming mitigation. (Image: Authors)FIGURE 8. Acquisition results after jamming mitigation. (Image: Authors)
FIGURE 9 shows the cross-ambiguity function (CAF) of PRN31 before jamming mitigation. It is obvious from the figure that the search space is quite noisy, and the receiver fails to acquire the GPS signal due to the difficulty of isolating the peak from the noise. However, FIGURE 10 shows that the peak clearly emerges from the noise floor and can be easily detected by the receiver after applying the jamming mitigation algorithm.
FIGURE 9. CAF of PRN31 before jamming mitigation. (Image: Authors)FIGURE 10. CAF of PRN31 after jamming mitigation. (Image: Authors)
These figures demonstrate the power of the proposed algorithm and confirms that the useful signal is not lost during the filtering process. Before applying the jamming mitigation algorithm, the receiver lost lock on all satellites and failed to provide a navigation solution. However, after applying the proposed algorithm, the navigation solution is available with an accuracy of about 10 meters in the east and north components and around 20 meters in the up component, as shown in FIGURE 11.
FIGURE 11. Navigation solution. (Image: Authors)
Conclusion
In this article, we have proposed a new method for mitigating a linear chirp narrowband jamming signal based on the WPT. Although the WPT has been widely used in the literature in the context of mitigating narrowband jamming, this technique is characterized by a significant computational complexity that is not only proportional to the length of the signal, but also proportional to the wavelet decomposition level.
The results show that our proposed algorithm is able to maintain excellent performance in the suppression of the jamming signal with a significant reduction in complexity. In the proposed technique, the sub-bands affected by interference are identified and are further decomposed to be used to reconstruct the jamming signal. Then, the useful GPS signal is obtained by subtracting the estimated jamming signal from the received signal. The performance of the algorithm has been assessed with respect to acquisition and navigation performance. The results show that the proposed algorithm successfully suppressed narrowband jamming without significantly degrading the useful GPS signal.
Acknowledgments
This article is based on the paper “A Novel Wavelet Packet-based Jamming Mitigation Technique for Vector Tracking-based GPS Software Receiver” presented at ION GNSS+ 2018, the 31st International Technical Meeting of the Satellite Division of The Institute of Navigation, Miami, Florida, Sept. 24–28, 2018. The research was supported by the Natural Sciences and Engineering Research Council of Canada.
Manufacturers
The simulation system used a Spirent Communications Inc. GSS6700 Multi-GNSS Constellation Simulator, a Spirent GSS8366 Interference Combiner Unit and a Keysight Technologies N5172B-503 Interference Signal Generator. The receiver front end used was a NovAtel Inc. FireHose D17088 prototype digital GNSS front end.
HAIDY Y. ELGHAMRAWY is a Ph.D. candidate in the Department of Electrical and Computer Engineering, Queen’s University, Kingston, Ontario, Canada. She received her M.Sc. degree in engineering physics and mathematics from the Faculty of Engineering, Cairo University, Egypt.
MOHAMED YOUSSEF is leading GPS/GNSS product development activities for Sony North America. He holds an interdisciplinary Ph.D. degree from the Department of Geomatics Engineering and the Department of Electrical and Computer Engineering, University of Calgary, Canada.
ABOELMAGD M. NOURELDIN is a cross-appointment associate professor in the Departments of Electrical and Computer Engineering at Queen’s University and the Royal Military College (RMC) of Canada in Kingston. He is the director of RMC’s Navigation and Instrumentation Research Laboratory.
The U.S. Maritime Administration issued an expanded advisory for GPS disruptions in the Middle East. The new advisory renews and repeats warnings for the eastern Mediterranean and adds the Port of Jeddah in Saudi Arabia.
Reports have also been filed with the U..S Coast Guard Navigation Center about disruptions in Israel’s Port of Haifa and the Straits of Hormuz.
Analysis by the Resilient Navigation and Timing Foundation and the non-profit firm C4ADS has also shown on-going disruptions in Russian waters of the Black Sea. Also, GPS jamming by Russia is suspected during a recent NATO exercise.
The armed conflict in Syria has been blamed for much of the disruptions off of its shores.
GPS jamming in support of illegal fishing is suspected by some as the cause of problems off of Port Said, and disputes over mineral rights has been suggested for the disruptions seen near Cyprus.
Disruptions in the Black Sea are suspected to be security measures associated with the travel of Russian government officials.
Map: U.S. Maritime Administration Advisory 2018-014-GPS
Text of Maritime Administration Advisory
2018-014-GPS Interference-Eastern Mediterranean and Red Sea
This revised advisory cancels U.S. Maritime Advisory 2018-007.
Reference: U.S. Maritime Alerts 2018-004A, 2018-004B, 2018-008A.
Issue: Multiple instances of significant GPS interference continue to be reported by vessels and aircraft operating in the Eastern Mediterranean Sea. These reports have been concentrated near Port Said, Egypt, the Suez Canal, and in the vicinity of the Republic of Cyprus. Additional instances of similar interference were reported in October 2018 near Jeddah Port, Saudi Arabia. This interference is resulting in lost or otherwise altered GPS signals affecting bridge navigation, GPS-based timing and communications equipment.
Guidance: Exercise caution when transiting these areas. The U.S. Coast Guard Navigation Center (NAVCEN) and NATO Shipping Center websites contain information regarding effective navigation practices for vessels experiencing GPS interference. The information reaffirms safe navigation practices when experiencing possible GPS disruption, provides useful details on reporting possible GPS disruption, and is intended to generate further discussions within the maritime community about other disruption mitigation practices and procedures. This guidance also recommends taking note of critical information such as the location (latitude/longitude), date/time, and duration of the outage/disruption, and providing photographs or screen shots of equipment failures during a disruption to facilitate analysis. The NAVCEN information is available at https://go.usa.gov/xQBaU.
Contact Information: GPS disruptions or anomalies should be immediately reported to the NAVCEN at https://go.usa.gov/xQBaw or via phone at 703-313-5900, 24 hours a day. The NATO Shipping Center has requested that instances of GPS interference also be reported to them using the format on their Cyber Interference link.
Cancellation: This message will automatically expire on May 2, 2019.
NATO conducted its largest military exercise since the Cold War in the frigid waters and icy mountains of Norway Oct. 25-Nov. 7.
During the final days of the Trident Juncture exercise, GPS signals guiding ships, aircraft, tanks, trucks and troops began to fail. Tracking screens flickered and positions were suddenly wrong from a few meters to hundreds of kilometers.
Civilian airliners, cars, trucks, cargo ships and smartphones operating in and around Norway and Finland experienced similar disruptions. Norway-based airline Wideroe told The Barents Observer that its pilots were reporting the loss of GPS signals when flying to airports in northern Norway and Finland. Airfields affected ranged from Kirkenes, on Norway’s border with Russia, to Lyngen in Troms, much further west.
Russia is the chief suspect of jamming the signals in reaction to the massive size and proximity of the military exercises. Russia also has recently conducted massive military exercises in the Baltics.
“It is possible that Russia has been the disrupting party in this,” Finland’s Prime Minster Juha Sipila told local media. “Russia is known to possess such capabilities.”
Trident Juncture involved all 29 NATO alliance members. Neutral Sweden and Finland also took part amid growing uncertainty over Russia’s ambitions in the tense region.
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