GPS technology is doing far more than helping us navigate or receive accurate time. It is now being used to fight the spread of the global COVID-19 pandemic.
Global navigation satellite systems are being used to collect big data on travel and contact, but they are also being used in more unconventional ways: for example, quarantine enforcement and sanitation technology.
Read on to learn about a few recent developments in the world of GNSS/GPS that are bolstering the battle against the novel coronavirus.
Electronic monitoring enforces quarantine
There is a surge of applying ankle monitors to track sick individuals and deter them from spreading the virus further. According to BloombergBusinessweek, one business is thriving because of it: providers of electronic ankle monitors.
Kentucky courts are requiring GPS ankle monitors for people who test positive for COVID-19 and refuse to self-quarantine. Kentucky couple Elizabeth and Isaiah Linscott were two of a growing number of people placed under house arrest after Elizabeth tested positive for COVID-19 and denied signing the Self-isolation and Controlled Movement Agreed Order, a health department document promising she would stay home.
Photo: Regulus Cyber
Elizabeth told Louisville television station WAVE 3 News that she did not sign because she disagreed with the wording of the document. She said that she was concerned about having to contact the health department before traveling, even in the case of an emergency.
“My part was if I have to go to the ER, if I have to go to the hospital, I’m not going to wait to get the approval to go,” she said.
A few days after Elizabeth refused to sign the paperwork, her husband opened their door to an entourage of law enforcement officers serving them with a Health Department order to wear ankle monitors.
“I open up the door, and there’s like eight different people, five different cars, and I’m like ‘what the heck’s going on?’ This guy’s in a suit with a mask. It’s the Health Department guy, and they have three papers for us. For me, her and my daughter,” Isaiah said.
The Linville family is now confined to a 200-foot radius. If they leave their designated quarantine area, their ankle monitors will alert law enforcement.
Alternative to prison
The number of people on house arrest in the United States and across the world has surged as corrections departments struggle to slow the spread of the coronavirus within prisons. An estimated 25 to 30 percent more people are wearing ankle monitors in comparison with a few months ago, according to Bloomberg Businessweek. The U.S. Federal Bureau of Prisons reported a 160 percent increase in home confinement from late March to July. European corrections departments have similarly put thousands of inmates on house arrest in the last few months.
“Demand has spiked everywhere,” BI Inc. monitoring equipment executive Robert Murnock said to Bloomberg. “We’re getting calls from different jurisdictions and other countries we’ve never worked with.”
Efforts to reduce crowding in prisons mean that the electronic monitoring industry is one of very few industries benefiting financially from the coronavirus pandemic.
“Coronavirus gives electronic monitoring companies an opportunity like they’ve never had before to expand,” parole reform expert James Kilgore said.
On Aug. 3, Singapore announced the rollout of electronic tracking devices to enforce quarantine. Travelers will be required to wear GPS and Bluetooth-powered tracking devices that notify authorities if quarantine is broken or the device is tampered with. The rule went into effect on Aug. 11 and applies to all incoming travelers — resident or nonresident — over the age of 12.
On Aug. 20, the premier of Western Australia, Mark McGowan , said his government could soon force people in hotel quarantine to wear electronic monitoring equipment if they are deemed a risk. “If we identify people who are potential flight risks or who might have a criminal history, we are looking at applying monitoring bracelets to them,” he said.
An estimated 25 percent to 30 percent more prisoners are wearing bracelets now compared to the pre-outbreak period. In the U.S., the Federal Bureau of Prisons has placed about 4,600 inmates in home confinement, a 160 percent increase since the end of March.
“Demand has spiked everywhere,” said Robert Murnock, vice president for partnership development at BI Inc., a provider of EM technology.
The emergency shift to electronic monitoring spurred by COVID-19 may foretell a long-term shift toward use as an alternative to prison time, reducing clutter and the risk of the virus spreading among inmates.
Photo: LeoPatrizi/E+/Getty Images
Contact tracing via mobile phones
Israel is using covert mobile phone data to track the spread of COVID-19. On July 1, the Knesset approved a bill temporarily reauthorizing mass surveillance of coronavirus-infected citizens by the Shin Bet, Israel’s internal security service. The original program lasted from mid-March to June 9.
The contact-tracing program works like this. When a patient is diagnosed with COVID-19, the Israeli Health Ministry provides their personal information — including their mobile number — to the Shin Bet. The Shin Bet then consults a classified database of every person who uses Israeli telecom services to determine who came into contact with the infected individual for more than 15 minutes at a time. After the Shin Bet sends information back to the Health Ministry, the Health Ministry notifies those people via text and tells them to self-quarantine.
The Shin Bet’s newfound role in public health enforcement is quite different from its usual focus. Former Shin Bet agents say the COVID-19 mobile phone tracking technology was originally developed as a counterterrorism measure, and the tracking system being used on Israeli civilians is almost identical to that used for suspected terrorists.
“It’s the same system, the same methods,” retired Shin Bet agent Arik Brabbing said to BBC. “We know that someone was here in the park. We can get from the [mobile phone] company all the details about the hour, the place, exactly the place… and we can understand who else was around.”
Supporters of the mass surveillance program, including Prime Minister Benjamin Netanyahu, argue that reduced privacy is necessary to curb the spread of the virus. However, the Israeli government has come under fire by opponents who claim that the program is intrusive and undemocratic.
Israel’s contact tracing procedures are more secretive than those of South Korea and Taiwan, other countries that mandate central mass surveillance. South Korea and Taiwan both enforce quarantines with mobile-phone tracking, and both have built publicly available COVID-19 data platforms.
The South Korean government has disseminated detailed — but anonymized — information about COVID-19 carriers, including their travel routes and treatment facilities. Citizens broadly support these measures — a testament to collectivism in Korean culture.
Civic engagement and enthusiasm for fighting the pandemic is also remarkable in Taiwan, where the public has been collaborating with the government on a town hall-style website called vTaiwan. Citizen-led initiatives, like a GPS-powered tool for tracking face mask supplies, have been applied nationwide.
Meanwhile in Europe, eight major telecom companies, including Vodafone and Orange, have been supplying anonymized metadata to the European Commission to model and predict the spread of the virus. In the United States, the Centers for Disease Control and Prevention is soliciting GPS data from mobile advertising companies rather than carriers themselves.
The two tech giants, Apple and Google, made it easier for health agencies to join its coronavirus exposure notification system, creating a new built-in app within iOS and Android. The app provides real-time notification to users when they are exposed to a sick person.
Virus-killing robots may roam the streets
GPS-based robots, drones and autonomous cars are being deployed to sanitize outdoor spaces, transport medical equipment, and announce safety information to the public.
Robots began rolling around the streets of Wuhan, the original epicenter of the coronavirus outbreak, as early as January. China was the first to deploy robots of this type, but India, Spain, France and other countries have followed in their footsteps. In addition to the chemical-spray approach, some companies are pioneering mobile disinfection robots armed with large ultraviolet-C germicidal lights.
Apollo, the autonomous vehicle company of multinational internet giant Baidu, has partnered with Chinese self-driving startup Neolix to transport food and supplies to Beijing Haidian Hospital. Every morning at 10:30 a.m, an unmanned car delivers meals to about 100 frontline workers. The process eliminates direct contact, protecting the safety of food service workers, hospital staff, and patients.
Zhangjiang Artificial Intelligence Island
A fleet of Apollo and Neolix’s unmanned cars is also responsible for disinfecting all roads on Zhangjiang Artificial Intelligence Island, an 100,000-square-meter industrial complex in Shanghai. The vehicles are loaded with up to 160 liters of spray disinfectant and can cover the island’s entire road system in about half an hour.
The vehicles at Zhangjiang AI double as nighttime surveillance bots. They patrol the island and make sure that guests are adhering to coronavirus protocols, alerting security personnel if they note suspicious activity.
In addition to using drones to spray disinfectant, South Korea’s government has leveraged the technology for public announcements. On July 4, 300 drones lit the sky above Seoul in a show of appreciation for frontline workers. The drones executed a 10-minute synchronized show that included images of face masks, hand washing, and social distancing.
Summary
As COVID-19 continues to ravage the globe, governments rely on GPS to track the virus, contain it, and fight against it. The battle against coronavirus is still being waged on a global scale, utilizing GPS as a weapon along with many other existing technologies.
The pandemic changed the world forever, and it also highlighted the power of tracking and monitoring location of people and machines. It is another testament to the immense reliance on GPS technology in our modern world.
The increased deployment of these technologies necessitates increased security measures, especially when public health is on the line. Regulus Cyber offers GPS Cybersecurity software. To read more about it, visit www.regulus.com.
UAV developments are taking flight across the globe.
In one development, older technology might enable new capabilities for a pseudo-satellite UAV. Meanwhile, new technology adds significant landing capability to an Air Force drone. Finally, further trials are expected to help develop drone operational procedures and regulations in India.
Spain’s Skydweller moves to Oklahoma
An unmanned aircraft builder from Spain — Skydweller — is setting up operations in Oklahoma. This latest outfit to relocate is establishing its headquarters in Oklahoma City to develop a pseudo-satellite vehicle with a large payload capability.
For anyone who has kept tabs on the Airbus Zephyr, the UAVOS ApusDuo, The Aurora/Boeing Odysseus, or the Softbank/AeroVironment Hawk30 high-flying drone programs, you might have noticed that the stratospheric pseudo-satellite business is not easy. None have yet made it to true operational status — loitering for months at +60,000 feet and living off only sunlight, while carrying significant payloads to provide communications services. That said, some trials to date have apparently been quite successful.
All those existing UAVs are huge, flimsy, flex-wing aircraft that take an inordinate amount of care to handle in the difficult phases of take-off and landing. Airbus’ second prototype crashed in Australia in October 2019, and several other companies’ earlier prototypes have crumpled somewhat when they inadvertently contacted the ground.
Now enter Skydweller. Skydweller is designed to carry a relatively large payload and fly persistently in the stratosphere.
The payload includes one or more communications relays: 4G/5G cellular, day/night full-motion video, satellite communication, and imaging radar. This looks like it could be one capable vehicle. The makers hope to capture business in commercial and government telecommunication, geospatial, meteorological and emergency operations. Skydweller has apparently been around since 2017 and has a lot of capability, so let’s see how they do with their new venture in Oklahoma.
If you were wondering where this technology came from, it is today’s carry-over of the famous around-the-world flight by the Solar Impulse aircraft from 2016, which circled the globe without fuel, using electrical power generated by solar cells on its wings.
GA Makes Improvements with Reaper
In another life, I was quite attuned to what it took to “automatically” land a passenger jet, so a recent release from General Atomics (GA) about improving the auto-landing system on Reapers (new-generation Predators) caught my eye. GA has a U.S. Air Force contract to update these unmanned reconnaissance/attack drones with the latest and greatest, so making a working system better is one of those improvements.
Actually, GA made three changes. The first enables the drone to divert to an alternate landing zone if the planned landing area is compromised — another word to express the possibility that hostile action or weather forced home base to send the vehicle elsewhere. Quite clever, in that the alternate site might not have a ground control station, along with someone who can fly the aircraft.
MQ-9A Reaper drone, (Photo: USAF)
The ground pilot at home base has to either enter coordinates for the new alternate landing zone and the aircraft flies there and lands itself, or he needs to overfly the landing zone so that the Reaper can collect its own waypoint with which it can automatically align and land.
The second improvement has increased the speed limit of the cross wind in which the drone can land
The third enhancement allows the drone to land heavier than previously — both essential elements of being able to divert in an emergency, when weather may be poor and the aircraft could be carrying unused ordnance and fuel.
All this is a far cry from landing civilian air transports with GPS-based guidance, which is much more restrictive and with a whole mess of mathematical probabilities of the unlikeliness/likeliness of failure. Not so much for a Reaper drone on a mission during a “time of unrest.”
Home Deliveries in India
For those of you eagerly waiting for Amazon to start speedy deliveries of your online orders by drone, or Grubhub to drop in with an order of curry in a package dangling from a friendly unmanned air vehicle in your yard, there may be hope… especially if you live in India.
Following our earlier report of anticipated food deliveries by drone in India, more trials are leading to regulations and control systems. Altitude Angel from the United Kingdom has teamed with Indian Sagar Defence Engineering for a series of beyond-visual-line-of-sight (BVLOS) drone trials.
Altitude Angel’s GuardianUTM platform will be used to monitor and control these flights through real-life scenarios. Scenarios include medical and cargo transport, surveillance operations, survey and mapping, and search-and-rescue operations. Sagar will operate the cargo carrying drones; feedback from the GuardianUTM system will enable the BVLOS flights.
While the Indian government has begun to grant permission for some commercial UAV undertakings, the intent is apparently to use the output from the Sagar/Altitude Angel BVLOS trials, taking place August through October, to help develop regulations for safe operation of drones over increasingly longer distances in Indian airspace.
To sum up, intellectual property from an around-the-world photo-voltaic airplane may become a substitute for low-cost satellite TV and Wi-Fi, while auto-land is old hat for a Predator cousin and the Air Force has gained even greater landing flexibility for a principle recon/attack drone.
Finally, we can expect at least one continent to get to regulations that allow drone deliveries to become a reality at last. As usual, there is a lot cooking in drone-land….
The Sagar Defence Spectre UAV. (Photo: Sagar Defence Engineering)
Altitude Angel, an unmanned traffic management (UTM) technology provider, is partnering with Mumbai-based Sagar Defence Engineering Ltd. in BVLOS trials supported by India’s Directorate General of Civil Aviation (DGCA).
Together, Altitude Angel and Sagar Defence have been selected by India’s DGCA to carry out beyond-visual-line-of-sight (BVLOS) drone operations. The results of the trials will help define India’s regulatory framework for unmanned aerial vehicles (UAVs) in routine UAV deployment.
Altitude Angel’s GuardianUTM platform will enable BVLOS drone flights around a multitude of real-life scenarios including medical and cargo delivery, surveillance operations, survey & mapping, and search & rescue operations.
The Union Government has recently begun the process of granting regulatory permissions to the operation of drones for commercial purposes.
On participating in the BVLOS trials Richard Ellis, Altitude Angel’s chief business officer, said, “The potential for UAV use in India is immense so we’re excited to be partnering with Sagar Defence on these BVLOS trials. The ability to fly safely and securely BVLOS will unlock the potential of drones not just in India, but across the world. With Sagar, we’re very much looking forward to showcasing our proven technology to demonstrate the amazing use-cases of drones.”
Mridul Babbar, Sagar Defence’s business development head added, “Sagar Defence Engineering and Altitude Angel, two highly skilled teams, coming together for the BVLOS trials is a very strong partnership and one we’re thrilled to be part of. The combination of our UAVs and Altitude Angel’s world leading UTM platform will undoubtedly help advance the prospects of BVLOS flight across India and beyond.”
The BVLOS trials are scheduled to take place from August through to October 2020.
The trials will further build on trials Altitude Angel has been involved in this year. The company served as the lead and umbrella UTM for the Lake Kivu Challenge, part of the African Drone Forum, which took place on the shores of Lake Kivu, Rwanda, in January.
Global mobile wireless standards body 3GPP has given its approval to the regional navigation system created by the Indian Space Research Organization (ISRO), known as NaVIC, reports The Times of India.
The approval was given for the system’s use in Rel-16 LTE and Rel-17 5G NR specifications, paving the way for wider commercial adoption of NaVIC and allowing it to be integrated with 4G, 5G and internet of things technology (IoT).
Once these specifications are adopted by Telecommunications Standards Development Society, India (TSDSI), IoT devices in India can make a switch from GPS to NaVIC.
Electronics companies can start designing and building integrated circuits and mass manufacture other products created to be compatible with NaVIC.
Lynx–Lawrence & Mayo, an Indian engineering equipment providers, has expanded its portfolio to include specialized drones, through its partnership with ideaForge, India’s largest UAV manufacturer.
As part of the agreement, ideaForge’s drones equipped with more than a decade of UAV design expertise, will be accessible to Lynx – Lawrence and Mayo for applications across urban development, weather monitoring and testing, agro-technology and other sectors.
Developed by a team of Indian Institute of Technology Bombay graduates, ideaForge has been celebrated for its innovations in the unmanned aerial vehicle (UAV) domain. It built a small, light autopilot in 2009 and India’s first autonomous quadcopter UAV. Their designs have led the development of world class, indigenous UAVs for security and surveillance, reconnaissance, mapping, photogrammetry and other industrial applications. Now, their UAVs even come with the capability to meet the DGCA regulations, to ensure NPNT compliance.
Through this partnership, Lynx–Lawrence & Mayo will have access to ideaForge’s fleet of UAVs for inspection, surveillance, traffic and crowd management, and disaster management. The fleet includes:
Switch UAV – A fixed-wing vertical take-off and landing (VTOL) hybrid for terrain-independent deployment with long range, high endurance and high altitude capabilities.
Q-Series UAV – Enterprise specialist VTOL quadcopter built with military design philosophies.
Ninja UAV – Lightweight and economical micro UAV built specifically for mapping and advanced surveillance.
Netra Pro – Rugged quadcopter for maximum redundancy in extreme conditions.
Netra V-Series – Field-proven UAVs for mission-critical applications, integrated with high zoom HD real-time video transmission.
“We at Lynx have been driving innovation and excellence in advanced precision equipment for engineering and industry. With the addition of innovative UAVs from ideaForge, we are delighted to add a range of advanced and futuristic drones to our portfolio,” said Glenford D’souza , Lynx senior general manager. “We will be strategically deploying these products to create an integrated and specialised service/solution offering to our existing and potential clients. We look forward to a long term and fruitful partnership.”
IdeaForge has deployed more than 700 systems and has trained more than 1,200 pilots in services including the Indian Army, Navy and Air Force; state police forces; Indian railways; and many more agencies.
IdeaForge drones have been used in defense and homeland security for border monitoring, anti-terror operations, counter insurgency operations, disaster management, traffic monitoring, campus surveillance, crowd management and more.
With the flexibility to customize their drones for an array of requirements, ideaForge also provides end-to-end UAV solutions and services to the industrial and commercial sectors.
The ground station will be built in Bengaluru, a city that is already home to the ISRO Telemetry, Tracking and Command Network (Istrac). Istrac will host the Russian ground station as well.
A memorandum of understanding was signed between the two nations in October 2016. In return, Roscosmos will host ground-measurement gathering stations in Russia for India’s NavIC, which will boost the operations of the IRNSS satellites.
Telit has introduced a new positioning module aimed at India. The SL869T3-I combines GPS with India’s NavIC (IRNSS) navigation system and the country’s satellite-based augmentation system (SBAS), known as GAGAN.
The SL869T3-I module enables the creation of high-performance position reporting and navigation solutions. It complies with Automotive Industry Standard 140 (AIS-140) — an Indian government mandate that requires the use of NavIC for vehicle location tracking devices in all public transportation vehicles, effective April 2019.
The SL869T3-I is a 16 x 12.2-millimeter module with an LLC package. It is provided with a single RF input for L1 and L5 bands. It also contains Flash memory, a low noise amplifier (LNA) and SAW filters. The RF front end is specifically designed to comply with sensitivity specifications contained in AIS-140 standard.
“The new SL869T3-I is among the first IoT GNSS positioning receiver modules to deliver IRNSS/NavIC L5 coverage compliant with the Indian government’s AIS-140 regulation,” said Manish Watwani, EVP global product management, Telit. “This new addition to the Telit family of products results from more than 20 years’ experience in GNSS applications.”
For more information, visit booth C3.227 at Electronica, Nov. 13-16 in Munich, Germany.
A new GPS + IRNSS module is being developed by Indian firm Ramakrishna Electro Component (REC) in partnership with STMicroelectronics and Shanghai Mobiletek, according to press reports.
The module will rely heavily on the Indian navigation satellite constellation IRNSS (also known as NaVIC), REC Managing Director Shivang Luthra told reporters at an event in New Delhi.
“There have been dependency of imported GPS module which use the U.S., European or Russian satellites,” Luthra said. “We have developed a GPS module, Utraq, that will mainly use the Indian satellites for GPS navigation.”
The module will be produced at a Shanghai Mobiletek factory in China, and the chips will be made by STMicroelectronics. REC owns the Utraq module and will roll it out in October for use in automotive end products. REC says the low cost of the chip compared to imports will make trackers more affordable in India.
The Indian government has mandated use of vehicle location tracking devices and one or more emergency buttons in public transportation vehicles; the mandate took effect April 1.
Utraq will be offered in two models: the L110 GNSS is a compact NavIC module, while the L100 GNSS module is a smaller-sized (patch on top) IRNSS module. Both modules can be used for tasks other than tracking, such as ranging, command, control and timing, and fo marine, aerial and terrestrial navigation.
DigitalGlobe has released pre- and post-event satellite imagery of the areas in India affected by heavy flooding.
According to the company, massive flooding devastated the Kerala state of India in late May and early August. At least 164 people were killed and more than 223,000 were displayed from their homes and are living in relief camps. In addition, Kerala has seen 40 percent more rainfall than normal since June, which has triggered landscapes in several districts.
In an effort to support disaster response and as a part of its Open Data Program, DigitalGlobe decided to publicly release the satellite images. According to the company, its Open Data Program supports the humanitarian community by providing critical and actionable information to assist response efforts.
By Paul Alves, Carmen Wong, Matthew Clampitt, Eric Davis and Eunju Kwak
INNOVATION INSIGHTS with Richard Langley
WE LIVE IN A POLLUTED WORLD. Sometimes even pristine environments are desecrated.
No, I’m not talking here about the rubbish on Mount Everest, nor the leaching of heavy metals from tailing ponds, nor the plastic trash in the oceans, nor the sulfur dioxide in the atmosphere.
I’m talking about radio-frequency pollution. Just as we would like to have our physical environment free of pollution for our better health and that of the ecosystem, we would like the radio spectrum to be free of pollution so that its users — virtually everyone on the planet — can have a better RF experience, whether it be when listening to the radio, using a cell phone or operating a GNSS receiver. We usually call RF pollution interference, or RFI for short, as it interferes with the signal we are trying to receive.
RFI can be accidental or deliberate, in which case we call it jamming. As a shortwave radio enthusiast, I am familiar with both types of RFI. Although the majority of the world’s radio stations attempt to coordinate their broadcasts to ensure that two stations don’t try to beam their signals to a particular area on the same or an adjacent frequency at the same time, it does happen, ruining reception. And if a country doesn’t want its citizens listening to certain foreign radio broadcasts, it might attempt to jam them as the Soviet Union did in the past and as China, North Korea, Cuba and several other countries still do.
In this month’s column, we look at GNSS interference. In many cases, GNSS interference is accidental, with a nearby radio device putting out a signal at a fundamental frequency or a harmonic, which lies within the passband of one of the GNSS frequencies.
It could be intentional, too, and we’ve all heard about GPS jammers including the so-called personal privacy devices that deliberately interfere with GPS signal reception. Is there any way to detect GNSS interference and to find its source so that remedial action can be taken? Yes and yes. A team of authors from NovAtel tell us how.
Interference is a growing concern among GNSS users, particularly in parts of the world where radio frequency transmission is not strictly regulated. Intentional interference and jamming is cheap and relatively easy to obtain in the form of personal privacy devices (PPDs). These devices can sometimes cause unintended interference and jamming to important infrastructure such as an airport. In this article, we describe a method for creating an interference map using the NovAtel OEM7 Interference Tool Kit (ITK). The ITK is capable of detecting and eliminating interference, and can be used to measure the power of a received interferer. When data is collected for an area around a static and continuously operating interference source, it can be used to map out the interference over the affected area. We overview a method for mapping the interference and, using a model of power loss over distance, creating a map of the interferer’s likely position. We also discuss simulated results and three case studies with live (real-data) interference sources from India, Canada and Japan.
NovAtel introduced the ITK in 2016. The ITK’s interference detection provides a list of sources, which includes an estimate of the frequency, bandwidth and power of the measured interference. It also provides the power levels across the entire frequency band of the front end. Either of these can be used as measurements of the received interference power levels. When the power levels for a given frequency are combined from multiple locations, they can be used to estimate the power and location of the interference source. The received power levels can also be combined to estimate the interference power as a function of location. The performance degradation experienced by one receiver at a given interference level can be extrapolated to other receivers at the estimated interference levels.
INTERFERENCE DETECTION
The ITK tools include the ability to visualize the power received across the input frequencies (front-end) bands. This can be used to quickly and easily identify any irregularities in the spectrum. These irregularities could be caused by internal interference, which is interference between electrical components introduced through hardware integration or installation. It can also be caused by external interference, such as by a PPD or other nearby radio transmitter.
The ITK’s detection feature identifies potential interference and provides a list of the interference power, frequency and bandwidth. This makes it easier for integrators to automate responses to potential interference without the need to scan the spectrum themselves. FIGURE 1 shows the received signal power and interference detection threshold for the GPS L1 frequency band. In this case there is no interference detected.
FIGURE 1. Received signal power (blue) and interference detection threshold (red) for L1.
The detection threshold is adjustable. However, if it is set too high, it can cause interference to be undetected; if it is set too low, it can cause false detection. For this example, a fairly low value was chosen because we were willing to manually identify the interference source and ignore any false detection.
The ITK also includes tools to mitigate interference, limiting or eliminating its impact. This includes a high dynamic range mode, which is effective in reducing the impact of interference. If this is not sufficient, then notch or low-pass filters also can be applied to completely cut out parts of the spectrum to neutralize the impact of interference or jamming.
FREE-SPACE LOSS
The mapping algorithm, which will be discussed later, requires a model of the power loss as a function of distance (d) to the transmitter. As the wave spreads from the transmission source, the power is lost according to:
(1)
where Lp (dB) is the power loss in dB, d is the distance in meters, and λ is the wavelength in meters. This equation can be expanded into a function of frequency (f, in Hz) and distance (d, in millimeters). Changing the units in this equation changes the constants.
(2)
For example, if the transmitter is broadcasting at 1.237 GHz, then Equation (2) gives
(3)
This ideal power loss is significantly increased by physical obstructions that are common, such as vehicles, buildings, trees or the terrain type. Different materials can have significantly different impacts on the power loss.
Some researchers have used a precomputed power map and map matching for indoor positioning. This method uses the expected received power to position a receiver. The same algorithm that is used to position the receiver could also be used to position the transmitter.
FIGURE 2 shows the received power as a function of distance that was observed for the Calgary test. There is a large variability in the power, likely due to natural obstructions.
FIGURE 2. Received power as a function of distance from the transmitter.
The equation for the line of best fit of this data is significantly different from Equation (3). This is likely due to the obstructions and limited number of data points. Due to problems with inaccuracies with this data fit, any further power calculations will use Equation (2).
MAPPING THE INTERFERENCE IMPACT
Using a single observation of the received interference power, a profile of the transmit power as a function of location can be created using a power decay curve similar to that shown in Figure 2. If we assume that the transmitter is at a given position and use the decay curve through the observed power, then we can estimate the transmit power at that location. When we do this for multiple locations, a power profile is created. This process is shown in FIGURE 3. When these plotted estimates are connected continuously, then we get a power profile.
FIGURE 3. Received power as a function of distance from the transmitter.
This power profile could pertain to a lower power transmitter that is relatively close to the receiving antenna or could be a stronger transmitter that is farther away. A single transmitter at any location could be responsible for the received power depending on the power of the transmitter.
When additional measurement points are added at different locations, the estimated powers of the transmitter for each individual observation can be combined. The estimated transmit power at some of the potential transmitter locations will match between the observations. For potential interferer locations that are far from the true transmitter location, the observations will conflict with each other.
Creating this type of power profile can be useful for pre-analysis. If we assume that none of the measurement locations can observe the interference, then the received interference must be equal to or less than the noise floor. If we assume that the received interference is at the noise floor, then we can use this profile map to identify the power of any hidden, undetectable transmitters in a region. An interferer may be broadcasting under the noise floor, undetectable at that power and distance. For example, if we want to monitor an area for interference around critical infrastructure, such as an airport, then we can deploy a network of ITK receivers. If no interference is detected, it is still possible for interference to be present if the power level of the transmitter is low enough that it does not reach any of the receivers above the noise floor. This analysis can be used to estimate the minimum detectable interference across the area, and used to determine the receiver network spacing and locations to ensure the minimum detectable interference is immediately detected.
FIGURE 4 shows an example of measurement points from the India case study. It shows the estimated power of a potentially undetectable interference source if no interference is detected anywhere at the measurement points. Lighter colors indicate a higher undetectable interference power. Notice how it is possible to miss a weak interferer that is close or a high-powered interference source that is farther away. This also illustrates how much information we can gather from zero-observation points where interference could not be detected.
FIGURE 4. Locations and power of possibly hidden interference sources that would be undetectable by observation points, shown as blue dots (Map data: Google, DigitalGlobe).
This method could be used to determine the path or spacing of receivers to monitor a region to detect interference at a certain level. With some history added into the model so that the uncertainty increased over time, a single receiver or a fleet of receivers could plan out their routes to monitor for interference.
The estimated interference source power can be used to determine the impact of the interference and give an estimate of the location of the interferer. A single static interferer will be assumed when estimating the location of the interferer using a goodness-of-fit model. A grid is created over the interference area. For each point in the grid, the attenuation (power loss) model is used to calculate the residual between the minimum transmit power and all power measurement points. If the residuals are low for all the observed power locations, then this is the most likely location of the interference transmitter.
FIGURE 5. Example of the goodness of fit for potential transmitter location and power.
FIGURE 5 shows an example of this goodness-of-fit test. The red dot shows the location of a potential transmitter location under test. Using the distance attenuation model, the predicted received power for each of the measurement points is calculated. The difference between the expected received power and the actual received power is an indication that this is not the correct transmitter location. The root-mean-square error of the fit error for all the observed points gives a likelihood that the transmitter is at this location.
SIMULATED RESULTS
Using the goodness-of-fit method, we can generate reasonable visualizations of the interference effect. FIGURE 6 shows an example map produced from simulated interference to the east.
FIGURE 6. Interference map from a simulation where the interference is on the east side (Map data: Google).
The expected power attenuation model matches perfectly with the data because it is a simulation. Similar results were obtained when the interference was assumed to come from the west and north. The yellow line shows a “roller-coaster” plot of the interference power. The height of the line shows the relative received power. Notice that it increases as we approach the source of the interference and decreases as the path moves away from the interference. A combination of the roller-coaster plot and the map give a quick visualization of the impact and location of the interference. There is a slight ambiguity between the east and west side of the road because the transmitter is close to the road. The goodness of fit works very well in this case to identify the location of the interference source.
FIGURE 7 shows a case where two interference sources are simulated. In this case, the model breaks down because it assumes that there is only a single interference source. The model clearly has difficulties determining the location of the interference. Even with accuracy issues, the model could still be used as a visualization of the interference that is easier to interpret than looking at numbers in a table.
FIGURE 7. Interference map from a simulation with 2 interference sources (Map data: Google).
INDIA DATASET
This dataset was the initial motivation for this work. A customer reported intermittent tracking problems with a newly installed receiver. The receiver would stop tracking for a few hours every evening. Customer service visited the site to investigate. Because of the intermittent nature of the problem, interference was suspected. An OEM729 receiver was walked around the affected antenna in an attempt to find the source of the interference and also to prove to the customer that interference was in fact the cause of the tracking problems.
FIGURE 8 shows the collected measurements. The numbers shown are the received interference powers at each location. It is possible to approximate the location of the interference and the impacted area by looking closely at the measurements, but it takes some close examination and interpretation.
FIGURE 8. Received interference power measured when searching for interference in India.
The source of the interference was identified using this approach. It was found to be a weather station, which performs a nightly upload of data collected throughout the day. This weather station broadcasts at 1580 MHz, which was jamming L1. The customer was able to move the interfering antenna to another site. The customer also could have used the ITK to apply a notch filter, which would have mitigated the interference’s impact, but it is better to remove the source of interference if possible.
Using the data points collected, an interference map can be generated using the method described. This map is shown in FIGURE 9. The lighter color indicates a higher likelihood that the interference transmitter is at that location. The location of the transmitter is also shown in the figure. The likelihood map is very close to the actual location of the transmitter. It gives a quick and easy-to-interpret visualization as opposed to individual measurement points.
FIGURE 9. Interference map for the India case study (Map data: Google, DigitalGlobe).
CALGARY DATASET
We were made aware of a potential unintentional L2 interference device and took it to Cross Iron Mills mall, north of Calgary, Canada, to investigate. FIGURE 10 shows a map of the area.
FIGURE 10. Map of the test area showing the location of the interference source.
We drove the path shown in blue to characterize the interference, and collected data using an OEM729 receiver with the ITK feature. Two buildings are near the interference source: a smaller building to the north and a large building to the south. These buildings block and shield the receiver from the interference when it is between the interference and the receiver.
The interference device was a transmitter to send video from a drone to a monitor, broadcasting at 1.2 GHz with 800 milliwatts. It was purchased online with no warnings about potential impacts it may have on other systems or devices. As recreational drones (and their electronics) become more popular, unintentional jammers and interference sources could become commonplace. We have no continuous monitoring and enforcement for short-range and short-duration unintentional jammers such as this one.
Although many commercial-grade receivers, such as ones common in cell phone and GPS watches, were unaffected because they only operate at L1, the box the device came in also indicates that there is a 1.5-GHz model capable of broadcasting at 2 watts. With 2 watts at 1.5 GHz, GPS L1 would be significantly jammed. This emphasizes the need for interference detection and mitigation. Nothing is stopping recreational hobbyists from accidentally jamming a significant number of users and services.
FIGURE 11 shows the roller-coaster plot of the interference observed during the test. The height of the yellow bars indicates the received power for the L2 interference. The power is generally higher closer to the interference source and decreases as a function of distance; however, there is a lot of deviation. Physical obstructions also cause significant decreases in received power.
FIGURE 11. Observed power of the interference source (yellow) over the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
For example, on the north end of the small building, shown on the right side of the figure, the observed interference power drops to almost zero despite being relatively close to the interference source. The large variations in power throughout the southern loop may be due to partial obstructions from parked cars or outcrops of the building. These physical obstructions cause larger decreases in received power than simply moving the antennas away from each other.
Since the interference was only broadcasting on L2, a position is still available through the other GNSS frequencies. The GPS receiver had difficulty tracking GPS L2 signals because of the interference.
FIGURE 12 shows the number of GPS L2 signals tracked. As the receiver approached the interference source, it became more and more difficult to track the L2 signals. As the receiver moved away from the interference, or behind a physical obstruction (like a building), the impact of the interference decreased and the signals were reacquired.
FIGURE 12. Number of L2 satellites tracked (red) over part of the test course (Map data: Google, Landsat / Copernicus, DigitalGlobe).
This shows how a simple device can inadvertently be harmful. Anyone could have purchased this device to transmit video from their recreational drone. Since this device only broadcasts on L2, the GPS of the drone and many nearby devices would have been unaffected, while almost completely jamming and disrupting any dual-frequency receivers nearby.
FIGURE 13 shows the interference goodness-of-fit map from the real data test. The map shows the correct trend, but the peak of the map does not include the actual location of the interference transmitter. This is due to inaccuracies in the power attenuation model. For example, a significant shift to the south is due to the rapid decrease in power when moving behind the north building.
FIGURE 13. Interference map from the real-data test.
When only the southern dataset is considered, we get a more accurate map, one not impacted by the northern building. This is because the attenuation model does not account for obstructions. The performance of this kind of model could be significantly improved with a model that includes the topography and buildings.
Despite the inaccuracy of the map to precisely locate the interference source, these simple model maps give a nice visualization of the interference.
TOKYO REAL DATA RESULTS
We received a report of interference in Tokyo, Japan, and took a receiver there to investigate. FIGURE 14 shows the maximum received power throughout the dataset. The interference around 1570.69 MHz is obvious and easily to identify in the figure.
FIGURE 14. Spectrum power level for the Tokyo dataset.
FIGURE 15 shows the observed power of the interference source when walking around the building. There is a peak in the received power when moving to one side of the building, while the observed power is relatively constant over the other three sides of the building. This strongly suggests that the interference source is along the one side of the building.
FIGURE 15. Observed power of the interference source (yellow) for the Tokyo dataset (Map data: Google, Zenrin).
This figure also shows the estimated goodness-of-fit interference map produced using the algorithm described earlier. The source of the interference could not be conclusively determined; however, we believe that the source was emanating from one of the vehicles in the parking lot.
This real example illustrates how useful this visualization of the observed power is in understanding the nature of the interference, identifying the source and localizing its effect. The interference in this case did not cause a noticeable change in the number of satellites or signals tracked.
CONCLUSIONS
This article showed a creative and useful application of NovAtel’s Interference Tool Kit available as a feature on the OEM7 line of receivers. The ITK can be used to create maps that show the estimated location of an interferer as well as the impact of the interference on other users. We demonstrated this using simulated datasets where the agreement between the simulated and actual loss-of-power models made for overly optimistic results. Three case studies are also shown: The original motivation for this work was a customer-service case in India. The second is a case in Calgary where unintentional interference was being caused by a drone video transmitter. The third dataset from Tokyo was a similar example, where, unfortunately, the true interference source could not be conclusively identified.
The three interference case studies show the importance of interference detection and mitigation because intentional and unintentional interference sources are easy to obtain and are not easily monitored or restricted. In one of these cases, a device that was naively purchased online as a UAV video transmitter ended up jamming GPS L2 in an area of roughly 2,000 square meters. With interference mitigation, it is possible to continue to work and operate in these environments without interruption or significant impact.
ACKNOWLEDGMENTS
The authors thank Bryan Leedham and Saravanan Karuppasamy for sharing their customer stories with us and providing us with the data for the case studies. This article is based on the paper “Interference Likelihood Mapping with Case Studies” presented at ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018.
Paul Alves received a Ph.D. from the Department of Geomatics Engineering at the University of Calgary in 2006. He is a principal research engineer in the Applied Research Team at NovAtel Inc. in Calgary, Canada.
Carmen Wong is a geomatics engineer at NovAtel. She received her B.Sc. in geomatics engineering with biomedical specialization from the University of Calgary in 2008.
Matthew Clampitt graduated in 2014 with a B.Sc. in geomatics engineering from the University of Calgary and is now a developer in the Positioning Algorithms Group at NovAtel.
Eric Davis has an undergraduate degree from the University of Calgary, with majors in both astrophysics and physics. He also earned an M.Sc. in physics at the University of Calgary. He joined NovAtel in 2016.
Eunju Kwak received her Ph.D. from the Department of Geomatics Engineering, University of Calgary, in 2013. She is a geomatics engineer at NovAtel.
FURTHER READING
• Authors’ Conference Paper
“Interference Likelihood Mapping with Case Studies” by P. Alves, C. Wong, M. Clampitt, E. Davis and E. Kwak in Proceedings of ION ITM 2018, the 2018 International Technical Meeting of The Institute of Navigation, Reston, Virginia, Jan. 29–Feb. 1, 2018, pp. 467–482.
• GNSS Interference and Jamming Detection
“Interference” by T. Humphreys, Chapter 16 in Springer Handbook of Global Navigation Satellite Systems, edited by P.J.G. Teunissen and O. Montenbruck, published by Springer International Publishing AG, Cham, Switzerland, 2017.
“Demonstrated Interference Detection and Mitigation with a Multi-frequency High Precision Receiver” by F. Gao and S. Kennedy 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. 159–170.
“Signal Acquisition and Tracking of Chirp-Style GPS Jammers” by R.H. Mitch, M.L. Psiaki, S.P. Powell, and B.W. O’Hanlon in Proceedings of ION GNSS+ 2013, the 26th International Technical Meeting of the Satellite Division of The Institute of Navigation, Nashville, Tennessee, Sept. 16–20, 2013, pp. 2893–2909.
• Radio Frequency Propagation Radio Frequency Propagation Made Easy by S. Faruque, SpringerBriefs in Electrical and Computer Engineering, published by Springer International Publishing AG, Cham, Switzerland, 2015.
• Localization Based on Signal Power
“Indoor Localization Based on Floor Plans and Power Maps: Non-Line of Sight to Virtual Line of Sight” by J.J. Khalifeh, Z.M. Kassas and S.S. Saab in Proceedings of ION GNSS+ 2015, the 28th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, Sept. 14–18, 2015, pp. 2291–2300.
SkyTraq Technology Inc., a fabless GNSS positioning technology company, has introduced the S1216F8-GI2, a NavIC + GPS/GAGAN receiver module for the emerging Indian market.
It integrates L1/L5 RF front-end and baseband processor capable of receiving up to 14 L5 NavIC signals and up to 20 L1 GPS/GAGAN signals simultaneously. With currently usable six NavIC signals and three GAGAN signals, it offers a total of 18-23 usable signals for navigation compared to 9-14 usable signals with conventional GPS receivers, providing improved accuracy in urban canyon environments with signals often blocked by high buildings.
The S1216F8-GI2 has form-factor and pin-out compatability with popular 12 x 16-millimeter GPS receiver modules, so customers using those GPS modules can effortlessly migrate to NavIC/GPS capability by drop-in replacement and changing to an L1/L5 antenna.
For emerging intelligent transport systems (ITS) applications requiring NavIC/GPS capability in India, S1216F8-GI2 enables fast time-to-market for product manufacturers, the company said.
NavIC sub-frame data output is a useful feature of the S1216F8-GI2. It can output NavIC broadcast warning messages related to weather alerts, forecast, and natural disasters such as cyclones, earthquakes and tsunamis.
An S1216F8-GI2 engineering sample, evaluation kit and datasheet is available. Volume delivery to customers begins in late March. The S1216F8-GI2 is manufactured with ISO/TS 16949 automotive certification.
The Jawaharlal Nehru Technological University-Hyderabad (JNTU-H) and Hexagon Capability Centre India (HCCI) have established a GNSS laboratory at the Centre for Spatial Information Technology, JNTU-H, reports Telangana Today.
The university is located in Kukatpally, Hyderabad, in the Indian state of Telangana.
The lab is equipped with NovAtel GNSS receivers, antenna, systems, cables and other hardware components. The equipment enables reception, processing, analysis and development of navigational data and applications to augment curriculum for JNTU-H students for research and education.
The establishment of the GNSS lab will also provide an opportunity to the students, scholars and faculty members to carry out research in satellite-based navigation and to develop advanced applications.
HCCI will provide internship to the students with financial support and job opportunities. This provision will not only be for CSIT students, but also for students with geo-informatics background from other constituent units of JNTU-H.
After opening the lab, Michael Kinahan, the software director of Hexagon Positioning Intelligence (NovAtel products division of Hexagon group) discussed various technical aspects of the NovAtel products with the potential of applying high-precision positioning capabilities to solve real-world challenges.
