Tag: Texas

  • Tracking the Whirlwind: Mapping tornadoes using GIS

    Tracking the Whirlwind: Mapping tornadoes using GIS

    3:13 a.m. Pulsing alarms. NOAA weather alert: TORNADO WARNING! TAKE IMMEDIATE SHELTER!

    Without hesitation, the family awakened from their sleep, grabbed wallets, smartphones, car keys and hurriedly descended the stairs into the shelter. Doors sealed, the children crawled into their shelter beds.

    The mother and father, listening to the weather radio, heard their county’s name in the emergency broadcast. They looked at the smartphone’s weather map blinking with the text alert. A large swath of rain covered the area, painting yellows and reds inside a field of green. At the trailing edge of the storm, where skies were beginning to clear, the storm’s red tail began curling into a ball, moving directly toward them. Inside the ball, a dark red deepened into a growing magenta core. White pixels appeared within the magenta tail. Its path was unchanged and it was closing.

    The man and woman huddled together watching the storm radar app on his mobile device not thinking about how their situational awareness is a confluence of spatial wizardry and atmospheric thermodynamics. The WSR-88D NEXRAD (Level III) radar scans a 143-mile radius, sweeping 14 elevation angles every five minutes to create a composite view of the surrounding weather. Colors correspond to the intensity of reflected hydrometeors (forms of precipitation) ranging from 0 dBZ, light rain in blue and green, to 75 dBZ, hail in magenta, and at 95 dBZ, it is physical debris carried aloft showing as white. Assembling the radars from across the country creates a seamless national weather mosaic (weather.gov/Radar). The dot on the smartphone’s weather app marking their own position is GNSS, orbiting far above.

    In his hand both the NEXRAD and GNSS are blended in real-time as he watches the Tornado Vortex Signature (TVS) move toward his family and his house. Beyond the closed shelter doors, tornado sirens wail, mixed with peals of thunder. The warnings are no longer county names but names of towns. There are people for whom such a moment is not hypothetical. Scott Bagenzie knows exactly what comes next, not from imagination but from experience.

    On Monday, May 20, 2013, at 2:56 p.m. Central Time, an EF5 tornado touched down northwest of Newcastle, Oklahoma, rapidly intensifying as it carved a path to Moore. The tornado lasted 36 minutes and covered 17 miles (FIGURE 1). Scott was caught by it, and I had the privilege of hearing him tell me what it is actually like to be inside those moments of sheer terror the rest of us only read about. He left work at 2:15 p.m. despite National Weather Service warnings for the counties flanking Oklahoma City. As he closed his car door, the sirens at the Mike Monroney Aeronautical Center went off. Security tried stopping him. He drove anyway.

    “I was dodging cars left and right as people were taking pictures out to the southwest. I called Mari and said, hey, I’m running to the house to make sure the pets are taken care of. And she said, You crazy ***, take care of yourself.”

    He pulled into his driveway, secured two cats in the closet and the dogs in the front bathroom, then stepped outside to see where the tornado was. His neighbor, who had an underground shelter in his garage, called out from next door: Get in over here! Scott went. As soon as the latch clicked behind them, debris began hitting the house above.

    Weather as GIS

    Weather is the most common topic of greetings. It is often the front page on newspapers. Television news is incomplete without a weather report, and weather is among the most downloaded apps on smartphones.

    In many ways, the first GIS was weather, starting in the mid-1800s, long before computers, GNSS and GPS, hand-plotting data points, and then hand-drawing lines of equal pressure, temperature, humidity and winds on charts.

    In the 1990s as a U.S. Navy weather specialist, I drew these charts by hand, plus four upper air charts learning how 3D spatial volumes interact. That was manual GIS. Now, in 2026, weather continues leading geospatial innovation via phased array radars, dual-pole radars (horizontal and vertical scans), acoustic atmospheric sensors, and predictive modeling for weather and climate, all of them layering atmospheric data using complex algorithms to forecast a dynamic fluid medium moving over an irregular spinning sphere that is unevenly heated. It is remarkably accurate, pushing the edges of geospatial predictive modeling.

    The architecture of violence

    The primary driver of powerful tornadoes is atmospheric thermodynamics unique to North America. Dry air crossing over the Rockies, cold arctic air pulled south by the jet stream, and warm moist air drawn north from the Gulf of America converge in a cauldron that can boil a normal convective storm into a sustained mesoscale supercell producing EF-5 tornadoes, the most powerful on record. Even though they make up less than one percent of all tornadoes, it is rare for EF5 tornados to occur anywhere else on Earth.

    The Enhanced Fujita (EF) scale for measuring them was developed in 1971 by Theodore Fujita, a Japanese engineer whose forensic study of atomic bomb blast damage at Nagasaki and Hiroshima led to his damage-based framework for measuring tornado intensity.

    FIGURE 2 This NOAA chart shows a height of 250 millibars (mb) of pressure over Tornado Alley
in the U.S.  (Credit: William Tewelow | Chart from NOAA NWS)
    FIGURE 2 This NOAA chart shows a height of 250 millibars (mb) of pressure over Tornado Alley in the U.S. (Credit: William Tewelow | Chart from NOAA NWS)

    The jet stream, a river of air riding a thermal pressure gradient in the upper atmosphere, creates vorticity as cold dense arctic air plummets south, wedging beneath the warmer Gulf air and forcing it upward along the frontal boundary, before the jet stream curves back north. FIGURE 2, the 300 mb (mb stands for millibars of pressure) chart, shows this process has caused a low pressure over Texas sitting in a 1,200-foot-deep ravine. A jet streak will form as air rushes into the ravine increasing the jet stream’s speed, which draws in rising convection currents that can spawn mesoscale storm cells and set up the potential genesis of severe tornadoes.

    When a funnel cloud forms, it is the visible physics of pressure dropping the temperature to the dew point causing condensation. The dropping pressure forms a bowl shape. Air flows into the dropping pressure, and the base of the cloud rotates cyclonically. As the rotation increases, centrifugal force of the colder dense rotating air pushes out the warmer higher-pressure air, further lowering the pressure at the core and deepening the bowl. That continues as the base descends into higher pressures at the surface, tightening the bowl into a cone. The difference in pressure between air outside the cone and what’s inside the vortex core can be 100 mb. That is basically a hole and wind rushes in to fill that void, but centrifugal force acts against the air. A tornado is born.

    Wraiths of destruction

    On May 31, 2013, 11 days after Moore, a multiple-vortex tornado formed near El Reno, Oklahoma. Along its periphery, small vortices spun around the rotating edge, circling, combining, breaking apart, vanishing and reforming, like wraiths of destruction dancing in a ring. The column darkened, descended and enveloped its own micro-vortices, forming the largest tornado ever recorded: 2.6 miles wide at its base.

    It grew so rapidly that experienced TWISTEX storm chasers attempting to place instrument disks behind it were consumed as it expanded from 1.6 miles to 2.6 miles wide. A father, his son, and a colleague were killed; their car was found eight miles away.

    Storm chasers are not thrill-seekers. WSR-88D NEXRAD, even at its lowest scan angle, already sits at 14,000 ft at its range limit because of the Earth’s curvature; spotters provide the ground truth radar cannot. Instruments such as Ground-based Local Infrasound Data Acquisition (GLINDA) extend that capability further: Tornadoes produce infrasound as low as 0.5 Hz, with a correlation between tornado size and frequency that may one day provide an early warning radar cannot.

    I asked Scott whether he felt the tornado before he heard it.

    “I couldn’t feel it,” he said, “but I could hear the sound of the train coming.”

    I pressed him to describe it beyond the cliché. He thought for a moment, then said, “It’s not a cliché. That is what it sounds like. It sounds like a freight train, and the sound of the house being torn apart.”

    The roar grows

    Back in the shelter, the physics unfolded exactly as Scott described. Unaware of the sensation, a deep groaning sound resonates miles ahead of the tornado. A low constant roar grows louder as it approaches. Explosions pop as transformers blow. The shelter is pitch black except for the phone screen, that small glowing window showing a white ball of catastrophe moving toward them. The roar grows louder. Ears pop. Temperature drops. The house shakes. The roar of the freight train is so loud the screams inside the shelter cannot be heard. The doors rattle. The whirlwind is trying to break in. Then the roar fades, almost to silence, an eerie quiet.

    In Scott’s shelter, the sequence was identical. His ears popped suddenly and painfully; they hurt for a full day afterward. In an EF5 tornado, pressure drops from roughly 950 mb in the surrounding air to 850 mb at the vortex core. The 100 mb passing over him was equal to a 3,000-ft pressure drop. It is the equivalent of instantly ascending two Empire State buildings stacked on top of each other, like falling straight up into the sky. Fighting against that force, Scott and his neighbor held shut the shelter latch as the doors bounced on their hinges.

    “I don’t know how well those are constructed. I didn’t take any chances.”

    Nearby, employees sheltering in a bank vault were physically holding the vault door closed as the tornado passed a thousand feet away. The vault’s timed lock could not engage. Five or six people leaned against a door designed to stop a robbery, fighting powerful thermodynamic forces.

    Then Scott no longer had to hold the latch. The truck on the other side of the garage wall had been pushed against the hatch from outside, pinning them in. When they finally forced it open and stepped out. There was nothing.

    “She just started screaming. She said, ‘No way, it didn’t do that.’ I told her, yeah, there’s nothing left.”

    The entire event, from first debris strike to silence, lasted roughly one minute. At 28 miles per hour, a tornado traverses one mile in two minutes, plowing through a neighborhood in seconds.

    Mapping the aftermath

    The question the rest of us ask from a safer distance is: What is the true pattern of destruction across time and geography? To answer it, I built a Tornado Severity Index (TSI) using National Weather Service tornado data. On average, there are 970 tornadoes per year, 81% are EF0 and EF1; 18% are EF2 and EF3; and the catastrophic EF4 and EF5 make up 1%.

    The NWS database reports the start and end coordinates, path width, magnitude, fatalities, injuries, and damages to property and crops. Working with the coordinate pairs, I calculated the distance and radial bearing of each path. But the EF scale alone tells only part of the story: A powerful tornado crossing an empty field and a moderate tornado crossing a dense neighborhood are not equivalent human events.

    I did not want the TSI to be another version of the EF scale, so the weighting was based entirely on the human toll. The formula is total fatalities (F) at 100% plus injuries (I) at 10%, =F + (I x 0.1) and normalized on a scale of 1 to 100. Economic damage was originally part of the equation, but the data are inconsistent and unreliable across reporting jurisdictions.

    FIGURE 3 The Tornado Severity Index (TSI) takes the human cost into account. (Credit: William Tewelow)
    FIGURE 3 The Tornado Severity Index (TSI) takes the human cost into account. (Credit: William Tewelow)

    The resulting composite doesn’t measure the strength of tornadoes, but rather their human impact (see FIGURE 3). The dataset of tornadoes from 1950 to 2024 is 71,813. Filtering it down to those tornadoes that had a human consequence where the TSI>1 reduced it to 2,362 tornadoes. I reduced it further to 1,625 including only those with one or more fatalities. This was made into a heatmap. The data were further reduced to 301, only filtering out all except where TSI>10. The heatmap color scale was weighted to the TSI Score. It shows where the highest concentration of intense tornadoes occurs.

    The results confirm Tornado Alley from Texas up through Oklahoma, and it also reveals Dixie Alley, an even more destructive corridor of severe tornadoes over Mississippi, Alabama and Tennessee. These areas align with the deep spring meridional jet stream discussed earlier. The northern side of the jet stream enhances cyclonic flow for storms in the area. The peak region of vorticity is where the jet stream turns back north again over Dixie Alley. Additionally, the rising terrain in that area causes orographic lifting and more rain, many times hiding the tornadoes within the pouring rain.

    GIS reveals what the physics predict: a narrow corridor of atmospheric geometry where conditions for catastrophic tornadoes are optimized, running through the same communities, year after year.

    For the sake of context, the Joplin, Missouri tornado on May 22, 2011, that caused 158 fatalities, 1,150 injuries, and damages of $2.8 billion ranks at the top of the TSI. The Moore tornado only scored 16.6 due to far fewer fatalities.

    The dataset reveals the physical signatures of severe tornadoes. On average, they peak in mid-May at 5:30 p.m. with a strength of EF4.2, carve a path 36 miles long and 2,073 feet wide, and each one causes 13 fatalities, 173 injuries, and losses of $71.5 million. Severe tornadoes do not travel west. They do travel a spectrum where most of them fall within a range from 016° to 060° with an average path of travel northeast at 031°. This is why Scott was right to question the reports of the El Reno tornado tracking southeast: What appeared to be southward motion was lateral growth. The tornado was not moving south; it was becoming enormous.

    “Pretty much sucking everything up,” Scott said, with confidence born out of his experience.

    The pattern and the person

    The TSI heatmap is a record of moments like Scott’s, representing a convergence of humans caught up in brutal atmospheric physics, where air becomes violent. The science explains the experience. It cannot prevent the next EF5; the thermodynamics will prevail.

    What GIS adds is pattern, memory and prediction. The TSI with directional analysis gives emergency managers, planners and underwriters insights for understanding where storm physics and humans intersect most acutely, and therefore where shelter codes and warning systems must be most robust.

    The family in their shelter, watching the white dot approach on the glowing screen, is experiencing the culmination of decades of geospatial and meteorological investment: NEXRAD networks, GNSS constellations, real-time data fusion in a consumer app. But as Scott will tell you, the most important instrument was the steel latch on the shelter door, and what mattered most was the neighbor who held it open for him as the tornado approached.

    Tornadoes are Earth’s thermodynamic engines of absolute chaos.

    “I’m not interested in tornadoes,” Scott told me. “Once burnt, you don’t play with the matches anymore.”

    Scott moved out of Oklahoma in 2013. The science is fascinating. People press right up to the edge of it, but the experience when science becomes personal is sheer terror.

    Live tracking tornadoes with GIS census tracts can know in real-time the impact on populations to immediately begin rescue operations, clean-up and recovery.

    GIS cannot capture the whirlwind, but it can track the most violent of them: northeast at 031°, seven football fields wide for 36 miles.

  • Balboa Geo demonstrates PNT system in GPS-denied environments

    Balboa Geo demonstrates PNT system in GPS-denied environments

    Balboa Geo, in partnership with the Texas A&M Engineering Extension Service (TEEX) and the George H.W. Bush Combat Development Complex (BCDC), completed a rigorous field testing campaign of its POINTER system, a “dual-use,” real-time alternative positioning, navigation and timing (A-PNT) technology designed for GPS-denied, degraded and disrupted environments, including indoor, subterranean and obstructed urban settings.

    The POINTER field test plan, led by Balboa Geo’s Andrew Aubrey, Ph.D., with technical support from TEEX and Texas A&M Professor Stacey Lyle, Ph.D., RPLS, involved 130 tests across seven challenging testing and training venues located at TEEX and the BCDC.

    Test venues included:

    • A three-story concrete structure with 10-inch-thick, rebar-reinforced concrete walls
    • A compartmentalized steel-hulled ship with three decks reaching approximately 25 ft high
    • A steel shipping container (CONEX)
    • A simulated collapsed structure and rubble pile composed of steel, concrete, and a 90° tunnel network
    • A simulated industrial oil refinery with processing equipment and complex, elevated steel piping
    • A six-story steel training tower with metallic siding throughout
    • The BCDC military-grade subterranean tunnel network, featuring a main tunnel at about 10 ft deep and a heavily shielded segment with Faraday cage properties simulating greater depth

    Rigorous test design and real-time A-PNT data collection

    The POINTER field test plan deployed a Base Station Laptop (BX) and a single Transmitter (TX) emitting an omni-directional Magneto-Quasistatic (MQS) field outside each venue. Two Receivers (RX) were introduced at various internal locations to capture multiple “XYZ” axis measurements within each GPS-denied setting. Tests were repeated to validate reproducibility, with highly precise measurements taken where possible for ground truth position references.

    The BCDC military-grade tunnel network testing consisted of “normal” and “inverted” configurations. The “inverted” test consisted of placing the TX at depth within the tunnel network, with the BX and RX units located externally.

    Highlights of the summary results and key findings:

    • MQS field penetration and position location were achieved at all seven test venues.
    • Real-time, three-dimensional distance measurements were obtained for all 130 tests.
    • The mean positional uncertainty across all venues was 12.62 cm.
    • Positional uncertainty ranged from 2.5 cm to 36 cm, depending on venue complexity, receiver location, and transmitter-receiver distance.
    • Vertical measurements at the concrete structure showed uncertainties as low as 2.5 centimeters at a distance of about 11 m, and up to 24 cm at about 30 m.
    • The POINTER system demonstrated penetration into and out of the BCDC military-grade tunnel network, including the shielded portion, indicating flexibility and performance in challenging subterranean environments.
  • Celebrating 50 years of GPS: An evening with the father of GPS

    Celebrating 50 years of GPS: An evening with the father of GPS

    PhotDana Goward, President of the Resilient Navigation and Timing Foundation, introducing Brad Parkinson and Matteo Luccio, GPS World EIC. (Image: GPS World staff)
    Dana Goward, President of the Resilient Navigation and Timing Foundation, introducing Brad Parkinson and Matteo Luccio, GPS World EIC. (Image: RNTF)

    On December 5, in Houston, Texas, at a gala event to celebrate the 50th anniversary of GPS hosted by the Resilient Navigation and Timing Foundation, Matteo Luccio, Editor-in-Chief of GPS World, interviewed Brad Parkinson.

    Here are two excerpts from the interview:

    How does GPS today differ from the design that came out of the Lonely Halls meeting 50 years ago this past September?

    Well, I’m very proud of what happened because, to my knowledge, there is no fundamental difference. Basically, that fundamental design has held up. … As a matter of fact, I still have one of the old Trimble handhelds, it’s called an EnsignGPS. It was one of those little devices that got shipped to the Iraq War. The other day, I pulled it out, batteries were kind of crummy, I got those squared away and went out, sure enough and navigated. I probably hadn’t pulled it out in at least 20 years. The point of the story is that evidently it still works.

    What do you consider the most significant impact of GPS on society?

    Well, the most significant impact is also probably the most perilous: kids today just take it for granted. They know where they are.

    Watch the full interview below. 

  • Celebrating GPS: An evening with the father of GPS

    Celebrating GPS: An evening with the father of GPS

    Artist's depiction of a GPS IIA satellite in orbit. (Image: USAF)
    Image: USAF

    GPS turns 50 this year, marking five decades of transforming the world in ways that have profoundly impacted society. Since its approval as a program on December 17th, 1973, GPS has revolutionized the way we navigate and comprehend our world, often in ways few realize.

    To honor this achievement, a special event will be held at the South Shore Harbor Resort and Conference Center in Houston, Texas, on December 5, at 6:00PM. This event aims to be a historic tribute to GPS’s journey and its impact on the global community.

    At the special event, Matteo Luccio, editor in chief of GPS World, will lead an engaging discussion with Brad Parkinson, the original chief architect of GPS, shedding light on the system’s early days, its far-reaching impacts on humanity, and exciting prospects for the future.

    Members of the press, federal employees, Resilient Navigation Timing Foundation members, PNT Advisory Board members, and presenters may attend the event for free. Others can secure their attendance for $75, which includes an optional one-year membership in the RNT Foundation.

    To reserve your spot, RSVP at [email protected] no later than November 27.

    The President’s National Space-based Positioning, Navigation, and Timing Advisory Board, which advises the government on GPS and related issues, will meet the following two days in the same location. Members of the public are welcome and encouraged to attend. Click here for more information on that event.

    Check back to watch a recording of the interview!

  • Autonomous vehicles connect the world

    Autonomous vehicles connect the world

    Image: gorodenkoff iStock/Getty / Images Plus/Getty Images
    Image: gorodenkoff iStock/Getty / Images Plus/Getty Images

    Autonomous vehicles are a truly fascinating innovation. Most modern vehicles on roadways around the world have some level of autonomy, ranging from Level 1 features such as cruise control to Level 5 fully autonomous features such as the ability to monitor roadway conditions and perform safety-critical tasks without intervention by a human driver.

    Even though autonomous vehicles have been continually developed and tested for years, adoption has been minimal. According to the University of Michigan Center for Sustainable Systems, a majority of researchers, manufacturers and experts predict widespread adoption of Level 5 autonomous vehicles by 2030 or later.

    Several barriers have delayed the adoption of autonomous vehicles, such as concerns about safety, data security and cyberattacks; lack of consumer demand; liability laws and lack of regulatory legislation; and doubts as to their economic viability.

    While their adoption is slow, autonomous vehicles have been widely praised for the range of benefits they would provide. According to the U.S. National Highway Traffic Safety Administration, they include: much greater road safety due to features such as advanced driver assistance systems, lidar, cameras, inertial navigation systems and more; greater independence for people with disabilities, senior citizens and low-income individuals; reduced road congestion due to the lower number of crashes and an increase in ride-sharing; and environmental benefits as the automotive industry transitions to all-electric vehicles.

    Several technology and automotive companies also have seen the potential benefits of autonomous vehicles for many applications and the potential impact they could have on communities worldwide. In response, these companies have supported autonomous vehicle innovation and adoption by offering new products and working closely with educators, nonprofit organizations and other groups who aim to leverage it to connect the world.

    Education meets automated racing

    Safran Electronics & Defense, which specializes in resilient positioning, navigation and timing (PNT) solutions, has advanced the adoption of autonomous vehicles with its simulation software while simultaneously supporting current students in their academic pursuits.

    To jointly develop future PNT technology and solutions Safran’s Minerva Academic Partnership Program supports partnerships with the academic community by providing its technology for student-led research projects that use GNSS signals. Leisa Butler, the program’s chair, elaborated on its mission: “Collaborating with our customers in academia while advancing PNT education is the program’s core purpose. We provide members with access to our powerful Skydel GNSS simulation engine.”

    Safran and auburn university students are pictured with their autonomous F1 race car that competed in the Indy Autonomous Challenge on the Las Vegas Motor Speedway at CES 2023. Auburn students used Skydel, a Safran simulation engine, to improve the capabilities of the car and to learn how to make it safe and reliable on the track. (Image: Safran Electronics & Defense)
    Safran and auburn university students are pictured with their autonomous F1 race car that competed in the Indy Autonomous Challenge on the Las Vegas Motor Speedway at CES 2023. Auburn students used Skydel, a Safran simulation engine, to improve the capabilities of the car and to learn how to make it safe and reliable on the track. (Image: Safran Electronics & Defense)

    As a part of the program, Safran has a long-established partnership with Auburn University’s College of Engineering. Safran and Auburn University students participated in the Indy Autonomous Challenge, which took place on January 7, at the Las Vegas Motor Speedway during the 2023 Consumer Electronics Show. Nine autonomous Formula 1 race cars, representing colleges and universities from around the world, took part in a head-to-head driverless racing competition with some vehicles reaching speeds of more than 190 mph.

    Safran has supported Auburn students before, during, and after this challenge by enabling them to leverage its GNSS simulators, such as Skydel and the GSG-8, which are used in the university’s autonomous vehicle lab. Butler said that giving students access to the simulation software prior to the high-speed races helped them troubleshoot and test the vehicles and improve the results.

    “Resolving issues in the lab improves safety while saving time and money,” Butler stated. “The Indy car features multiple antennas. Since Skydel can support multiple instances simultaneously, the team can test heading and realistic scenarios in a simulated environment. This is before they race next to other vehicles at high speeds.

    Safran also supports the general advancement of autonomous vehicle technology. Positioning and navigating autonomous vehicles involves the use of multiple technologies, including GNSS.

    “Skydel is a valuable tool for the autonomous vehicle industry that wants realistic lab testing because it can support multiple, independent trajectories or antenna outputs simultaneously,” Butler said. She also pointed to the importance of developing mitigation techniques against jamming and spoofing.

    “Using a simulator with the Skydel engine allows the user to test in all sorts of challenging environments before putting the wheels on the pavement. This lets the user make sure the vehicle is ready for real-world navigation and avoid costly mistakes. It also gives them a chance to practice and develop countermeasures against unintentional interference and malicious actors.”

    Butler added that Safran is proud to support students who are helping to develop automated technology.

    “Supporting Auburn’s Autonomous Vehicle team is an honor and a privilege. Student research represents the future of our industry,” Butler said. “We are proud to support them and see what they can accomplish with our simulation tools. We are confident that they will be able to gain valuable insights that will help them design, build and test their autonomous vehicles. It is our hope that their hard work will lead to the development of safe, efficient and affordable autonomous vehicles in the future.”

    Accelerating mobility

    Waymo, based in Mountain View, California, is an autonomous driving technology company. Formerly known as the Google self-driving car project, it was founded in 2009 and aimed to drive more than 10 uninterrupted 100-mile routes autonomously.

    Its first fully autonomous ride on public roads took place in 2015, then Waymo became an independent self-driving technology company in 2016. It launched its first public trial of autonomous ride-hailing vehicles, called Waymo One, in Phoenix, Arizona in 2017, and has expanded its completely autonomous ride-hailing service trials to Scottsdale, Arizona, as well as San Francisco and Los Angeles.

    The Waymo vehicle fleet also became fully electric this year.

    360° Lidar, Radar, and cameras make up most of the technical elements of the fifth-generation Waymo fully autonomous vehicles. They also have redundant steering and braking, backup power systems, redundant inertial measurement systems for positioning, and more. (Image: Waymo)
    360° Lidar, Radar, and cameras make up most of the technical elements of the fifth-generation Waymo fully autonomous vehicles. They also have redundant steering and braking, backup power systems, redundant inertial measurement systems for positioning, and more. (Image: Waymo)

    Driving Change

    According to its website, Waymo “represent[s] a diverse set of communities and interests, and we are coming together because we all share the belief that autonomous driving cars can save lives, improve independence, and create new mobility options.”

    Some of Waymo’s community partners include Bike MS, the Arizona Council of the Blind, the Foundation for Senior Living, and Mothers Against Drunk Driving.

    One community story to note is Waymo’s partnership with First Pace AZ — a supportive housing community for adults with autism, Down syndrome and other types of neurodiversity — to explore how Waymo could aid neurodiverse people.

    Eli is a resident of First Place AZ and an adult with neurodiversity. He does not drive and relies heavily on ride-hailing services, carpooling, and the train to get to work and to volunteer. Not all public transportation is always available or accessible at certain hours. Additionally, human-driven rideshare and carpooling services can present bias from drivers and other passengers who do not understand the behavioral nuances of people who are neurodiverse.

    To test the autonomous ride-hail Waymo One system, Eli and Natasha Grant, director of workplace and community inclusion at First Place AZ, hailed a ride to a local animal shelter.

    After using the Waymo One service, Eli believed Waymo’s technology could help him stay connected to his community, wherever he may live in the future. Grant added that autonomous vehicles provide independence for individuals who may otherwise not be able to go to places to which they want and need to go.

    Breaking social barriers

    Community partners that fight food insecurity use Cruise’s autonomous vehicles to pick up left over food from businesses. (Image: Cruise)
    Community partners that fight food insecurity use Cruise’s autonomous vehicles to pick up left over food from businesses. (Image: Cruise)

    Cruise is a self-driving car company based in San Francisco, California, and offers driverless rides in San Francisco; Austin, Texas; and Phoenix, Arizona. It was founded in 2013 by Kyle Vogt and acquired by General Motors in 2016.

    Cruise first offered driverless ride-share services for its employees in 2017. In early 2020, the company began testing those driverless rides on public roads in San Francisco. Later that year, Cruise switched gears and repurposed a portion of its all-electric autonomous vehicle fleet to deliver meals to the community during the COVID-19 pandemic. It also began self-driving delivery trials in Arizona.

    In 2021, Cruise announced plans for international driverless testing and expansion in Dubai and Japan. The next year, it opened its fully driverless service to public riders in San Francisco.

    Delivering Hope

    Cruise works with several community partners, such as the National Federation of the Blind, the SF-Marin Food Bank, and the San Francisco Giants.

    “At Cruise, our commitment to social impact is a vital part of our business and an extension of our mission to improve life in our cities, especially for people underserved by transportation today,” the Cruise website stated.

    In June, Cruise partnered with Replate — a nonprofit food rescue platform — to fight food insecurity and food waste in San Francisco and other communities. The partnership aims to use Cruise’s all-electric autonomous vehicle fleet, integrated with a national network of food recovery partnership from Replate, to pick up leftover food from local businesses and deliver it to organizations that help fight food insecurity.

    The goal of the partnership is to create a sustainable cycle of food rescue that fights hunger and waste in local communities.

  • GPS and AI collaborate on lifesaving emergency service solutions

    GPS and AI collaborate on lifesaving emergency service solutions

    Image: Kara Capaldo/iStock/Getty Images Plus/Getty Images
    Image: Kara Capaldo/iStock/Getty Images Plus/Getty Images

    Whether preparing for natural disasters or responding to everyday emergencies, first responders depend on the accuracy and dependability of GPS data to keep our communities safe. However, the increasing number and intensity of natural disasters, such as wildfires and hurricanes, and ongoing first responder staffing shortages have pushed the industry to look for ways to combine the tried-and-true benefits of GPS with new artificial intelligence (AI) technology to alert sooner, respond faster, and restore better than ever. The integration of AI’s adaptive learning capabilities with the ability of GPS to operate in areas of low or no connectivity make for cutting-edge emergency service solutions.

    New technologies incorporating both AI and GPS have already proven to save time and protect lives by quickly identifying and assessing potential fires. For example, in 2022, Sonoma County, California, used FireScout — an AI-powered fire detection solution — to monitor live footage for signs of fire and alert authorities. In one instance, the county found that FireScout’s AI solution detected and located — using GPS data — a fire 10 minutes before the 911 service was alerted about it, giving responders a head start on containing the fire. FireScout looks to integrate GPS functions more fully into their AI-enabled cameras with exact coordinate information. Investments in innovations that facilitate rapid response to natural disasters will lead to greater safety for first responders and their communities across the country.

    One way the industry is investing in GPS-powered AI innovation is through problem-solving competitions such as XPrize Wildfire, which encourages the development of cutting-edge solutions to wildfires. Teams will compete in one of two tracks: the Autonomous Wildfire Response track, which requires teams to combine AI and GPS data to differentiate between high-risk actual fires and decoy fires and then quickly suppress the real fires, and the Space-Based Wildfire Detection and Intelligence track, which requires teams to use satellites to accurately pinpoint fires across vast areas then relay that information to stations on the ground. GPS industry leader Lockheed Martin is providing a $1 million Accurate Detection Intelligence Bonus Prize to the winner of the XPrize Wildfire competition. Competitions such as XPrize Wildfire will result in products that can identify fires faster, reducing response times and minimizing damages to communities.

    Additionally, new GPS-powered AI solutions are bringing emergency resources to more people in the wake of hurricanes. In the aftermath of hurricanes, emergency personnel are tasked with identifying and allocating resources to restoration efforts. GPS-powered AI technologies such as the University of Connecticut’s hurricane monitoring system, compare pre-storm and post-storm satellite imagery to spot potential environmental and safety issues, such as flood water or damaged neighborhoods. The system then highlights those areas on a map and shares the coordinates of high-damage areas with emergency personnel. Services such as these support communities and allow restoration efforts to begin sooner with less risk to surveyors and responders.

    Beyond natural disasters, GPS also is being used with AI technology to shorten response times for emergency vehicles. Many towns, including St. Louis, Michigan, and Leon Valley, Texas, have implemented AI traffic light systems that use location data to detect the location of ambulances and fire trucks to give the vehicles a path of green lights, clearing out any traffic that might have slowed response times. Similarly, researchers at the University of Southern California are using UAVs — guided and tracked using GPS data — to carry automated external defibrillators (AEDs) to remote locations. These UAVs use coordinates provided by GPS receivers to operate in areas of limited connectivity and AI to determine the most efficient landing locations for different terrains. Ongoing research and further investment into the critical intersections of GPS and AI technology will help promote a safer future by supporting first responders and protecting communities in emergencies.

    The GPS Innovation Alliance (GPSIA) welcomes innovations in GPS and AI technologies that continue to revolutionize the way we respond to natural disasters and life-threatening emergencies. GPSIA is proud to support the expansion of these disaster-mitigating solutions by uplifting innovative research and design efforts, promoting new ideas, and ensuring adequate regulation is in place to protect users across the globe.

  • Integrity is integral to precision agriculture

    Integrity is integral to precision agriculture

     

    THE TREKTOR HYBRID ROBOT for agriculture, made by the French company SITIA, can work on a variety of crops by changing the width of its wheelbase and can perform many repetitive tasks, such as spraying and hoeing. (Image: SITIA)
    The Trektor hybrid robot for agriculture, made by the French company SITIA, can work on a variety of crops by changing the width of its wheelbase and can perform many repetitive tasks, such as spraying and hoeing. (Image: SITIA)

    Precision agriculture has been around for more than 30 years and now covers the majority of U.S. farmland. It refers to the ability of farmers to observe, measure and respond precisely to the variability of soil and crop characteristics within and between fields by using maps of these characteristics and GNSS navigation. It enables them to reduce inputs of seed, water, fertilizer, pesticides and fuel while increasing outputs. It also enables them to work at night and in the fog and automate many functions at large feed lots.

    For precision agriculture, GNSS integrity can mean the difference between, say, a robot protecting a vineyard by weeding and spraying pesticides or damaging it by straying onto the vines.

    Autonomous Tractors, Mowers, and Feed Monitors

    SITIA, a French company, has developed an autonomous tractor that is used by, among others, an organic vineyard in France’s Loire valley to tirelessly weed the narrow rows between the grape vines — compensating for the movement of young workers to cities. Thanks to the high accuracy and integrity of the Septentrio GNSS heading receiver inside, the autonomous tractor has decreased the damage to the vineyards by more than an order of magnitude compared to the traditional work done by a farmer with a manual tractor.

    Renu Robotics, based in San Antonio, Texas, makes a robot for vegetation management, called Renubot. It uses machine learning, a form of artificial intelligence, to plan its route, optimize its energy consumption, perform self-diagnostics, collect environmental data and assess the topography that it traverses.

    Navigation is based on a stored map of paths, a Septentrio RTK GPS receiver and sensors to avoid obstacles. A radio link enables the Renubot to communicate with a control center, for reporting and updates. When the Renubot returns to its recharge pod, it charges its lithium battery and performs updates and downloads.

    Manabotix Pty. Ltd., an Australian company, has developed an automated system to monitor cattle in large feedlots, using GNSS, lidar scanning and other vision or perception technologies and artificial intelligence. This has greatly improved the accuracy and consistency of feedlot volume estimates, which for the previous 150 years had been the responsibility of a select few employees, who would visually gauge the amount of feed in concrete troughs. This visual inspection by humans was inherently imprecise, subjective, and inconsistent, often causing animals to eat too much or too little one day and get off their optimal growth curve or even become ill. Manabotix’s solution consists of a Septentrio AsteRx-U GNSS receiver and antenna, a lidar scanner, and an onboard processing platform.

    Statistical Analysis

    Integrity is a key aspect of all these applications. A part of delivering integrity is a statistical analysis called receiver autonomous integrity monitoring (RAIM), which was developed for such safety-critical applications as aviation or marine navigation. A refinement of RAIM, called RAIM+, takes this analysis to the next level as part of a larger positioning protection package.

    For autonomous operation, it can be particularly hazardous to be overly optimistic about GNSS accuracy. This parameter is reported in the form of positioning uncertainty, which is the maximum possible error on the calculated position. It is especially necessary in challenging GNSS environments, where the receiver has a direct line of sight to only a limited number of GNSS satellites or where GNSS signals are degraded. RAIM alerts users when their receiver’s uncertainty strays beyond the limits they have chosen for their application.

    Users can be deceived by a consistent position or movement — which can be consistently inaccurate. The positioning uncertainty gives them an indication of the extent to which they can rely on their receiver’s positioning accuracy at any given moment. The receiver operator can set an alarm limit, so that the receiver can flag situations when positioning uncertainty becomes too large.

    The blue line in Figure 1 shows position uncertainty estimated by a GNSS receiver under favorable conditions, when the view of the sky is unobstructed, and the receiver has a direct line-of-sight to many satellites.

    Figure 1. Under good GNSS conditions, the position uncertainty shown by the blue lines is well within the alarm limits, indicating safe operation. The actual position of the receiver should always remain within the blue uncertainty boundaries. (Image: Septentrio)
    Figure 1. Under good GNSS conditions, the position uncertainty shown by the blue lines is well within the alarm limits, indicating safe operation. The actual position of the receiver should always remain within the blue uncertainty boundaries. (Image: Septentrio)

    During favorable conditions, the positioning uncertainty stays well below the alarm limit because the calculated position is almost the same as the robot’s actual position. However, in challenging environments, the truthfulness of positioning uncertainty becomes most critical (see Figure 2).

    Figure 2. In challenging environments receivers with high integrity report large positioning uncertainty, flagging possible inaccuracies to the system. If the receiver is too optimistic about its accuracy, the operation becomes hazardous. (Image: Septentrio)
    Figure 2. In challenging environments receivers with high integrity report large positioning uncertainty, flagging possible inaccuracies to the system. If the receiver is too optimistic about its accuracy, the operation becomes hazardous. (Image: Septentrio)

    For instance, when the view of the sky is partially obstructed by buildings or foliage, the receiver has access to only a limited number of GNSS satellites, making it harder to calculate accurate position. In such cases the receiver must report a higher positioning uncertainty, so that the system can take adequate action such as switching to lower speeds, staying further away from predefined boundaries, or stopping.

    A low integrity receiver may keep reporting an optimistic positioning uncertainty, that stays below the preset alarm limit even when the calculated position is way off from the actual position. The number may look fine, but effectively it becomes a “robot on the loose,” no longer on its planned path with a risk of damaging itself and its surroundings.

    Let us look at uncertainty limits in action during a GNSS car test in an urban canyon, where the view of the sky is partially obstructed by houses (see Figure 3). The orange lines are the positioning and its uncertainty boundaries reported by a Septentrio mosaic GNSS module in the car, while the red lines are the positioning and its uncertainty boundaries reported by another popular GNSS receiver. The white line shows the actual position of the car as it drives along the road. The orange uncertainty boundaries of the mosaic receiver are truthful and somewhat wider in this challenging environment, and you can see that the actual position always remains within these boundaries. On the other hand, the red trajectory jumps off course in a certain challenging spot on the road, with the actual position no more within the uncertainty boundaries, which remain too optimistic. In this case the competitor’s receiver gives a false sense of security and the system is unaware of its hazardous operation.

    Figure 3: In an urban canyon car test the Septentrio receiver reports truthful position uncertainty. A competitor receiver seems to be more accurate, while the actual position is not even within its reported uncertainty boundaries. (Image: Septentrio)
    Figure 3. In an urban canyon car test the Septentrio receiver reports truthful position uncertainty. A competitor receiver seems to be more accurate, while the actual position is not even within its reported uncertainty boundaries. (Image: Septentrio)

    If the receiver depicted by the red line provided navigational information for an ADAS automotive system, for example, this could mislead the system into thinking that the car switched lanes. If the system then attempted to correct the trajectory by switching back to the “correct lane” this would result in taking the car off course and potentially hitting the sidewalk or even another car.

    RAIM vs RAIM+

    The underlying mechanism behind truthful positioning uncertainty reporting is RAIM, which ensures a truthful positioning calculation based on statistical analysis and exclusion of any outlier satellites or signals. Septentrio receivers are designed for high integrity and take RAIM to the next level with RAIM+, guaranteeing truthfulness of positioning with a high degree of confidence.

    In Septentrio receivers RAIM+ is a component of a larger receiver protection suite called GNSS+ comprising positioning protection on various levels including AIM+ anti-jamming and anti-spoofing, IONO+ resilience to ionospheric scintillations, and APME+ multipath mitigation.

    Septentrio has fine-tuned its RAIM+ statistical model with more than 50 terabytes of field data collected over 20 years. It removes satellites and signals which may give errors due to multipath reflection, solar ionospheric activity, jamming and spoofing, while working together with the GNSS+ components mentioned above. Because of this multi-component protection architecture, it achieves a very high level of positioning accuracy and reliability which goes well beyond the standard RAIM. The RAIM+ statistical model is adaptive, highly detailed, and complete, taking advantage of all available GNSS constellations and signals. The full RAIM+ functionality is also available in Septentrio’s GNSS/INS receiver line. User controlled parameters allow it to be tuned to specific requirements.

    The diagram in Figure 4 shows RAIM+ in action during a jamming and spoofing attack on a Septentrio GNSS receiver. While AIM+ removes the effects of GNSS jamming, both AIM+ and RAIM+ work together to block the spoofing attack. Satellites with high distance errors, shown on the middle graph, are removed by RAIM+ since they do not conform to the expected satellite distance.

    Figure 4. In this scenario jamming gives satellite distance errors but is countered by AIM+ technology. During spoofing AIM+ eliminates some of the spoofed satellites, while other satellites that have wrong distances are dismissed by RAIM+ algorithms. (Image: Septentrio)
    Figure 4. In this scenario jamming gives satellite distance errors but is countered by AIM+ technology. During spoofing AIM+ eliminates some of the spoofed satellites, while other satellites that have wrong distances are dismissed by RAIM+ algorithms. (Image: Septentrio)

    This example shows that even in the case of jamming and spoofing, Septentrio’s high integrity receiver technology delivers truthful and reliable positioning on which any autonomous system can count.

    GNSS Design Around Reliability

    GNSS receivers designed to be reliable strive for high integrity in both reporting of the positioning uncertainty as well as in RAIM+ advanced statistical modelling. This ensures that these receivers provide truthful and timely warning messages and are resilient in various challenging environments. Other technologies such as inertial navigation system (INS) can also be coupled to the GNSS receiver to extend positioning availability even during short GNSS outages. Quality indicators for satellite signals, CPU status, base-station quality and overall quality allow monitoring of positioning reliability at any given time. High-integrity GNSS receivers provide truthful positioning in autonomous machines such as the SITIA weeding tractor. They are also crucial components in safety-critical applications, assured PNT and any other application where accuracy and reliability matters.

  • NV5 Geospatial maps North American shorelines and riverine environments

    NV5 Geospatial maps North American shorelines and riverine environments

     NV5 Geospatial has mapped more than 26 million acres of North America’s shoreline and riverine environments across more than 200 projects.

    The projects have spanned from the Nuyakuk River in Alaska, Lake Tahoe in California, the Rio Grande in Texas, the entire coasts of South and North Carolina, the Achigan River in Quebec, Chesapeake Bay in Maryland and the Florida Keys.

    In 2022, the company mapped and acquired topobathymetric lidar data for 14 projects including the Yellowstone River, Wyoming; Hells Canyon, Indiana; Revillagigedo Island, Alaska and Iles de la Madeleine in Quebec.

    NV5 Geospatial first mapped these environments in 2012 using high-resolution bathymetric lidar and natural color imagery. The company mapped 34,051 acres of shoreline along the Sandy River, located in northwestern Oregon, to study the ever-changing basin geomorphology.

    NV5 has also signed a two-year contract with the National Geodetic Survey of the National Oceanic and Atmospheric Administration to provide topobathymetric lidar, 4-band imagery and mapping of 3,115 sq miles of the Maine shoreline.

    “For a decade we have been helping local, state, and federal government agencies as well as commercial and private entities gain the insights they need to solve some of their most challenging nearshore and riverine projects through our mapping technologies including topobathymetric lidar,” Kurt Allen, vice president of NV5 Geospatial, said. “Whether it be mapping the shoreline after a hurricane, updating the national shoreline, assisting water boards with flood planning, or hundreds of other possible use cases, we are constantly improving our technology and scalability to always be at the ready for our customers.”

  • NASA partners with Firefly Aerospace for lunar GNSS mission

    NASA partners with Firefly Aerospace for lunar GNSS mission

    As a part of the NASA Commercial Lunar Payload Services initiative, Firefly Aerospace will land the Blue Ghost lander on the lunar surface in 2024. Onboard, the Lunar GNSS Receiver Experiment (LuGRE) payload will determine whether signals from two GNSS constellations can reach the lander and provide precise navigation on the moon for future missions.

    During a 12-day mission in the moon’s Mare Crisium basin, LuGRE will obtain the first GNSS fix on the lunar surface and receive signals from both GPS and Galileo. The LuGRE payload is managed by NASA’s Space Communications and Navigation program office.

    This payload is a collaborative effort between NASA and the Italian Space Agency to expand the capabilities of Earth-based navigation systems. Navigation engineers at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, have been testing the payload’s GNSS receiver and low noise amplifier. The receiver was developed and built by the Italian company Qascom.

    These components will be critical to LuGRE obtaining signals from the GPS and Galileo satellites. To prepare for operating on the moon, NASA engineers used a GNSS simulator to test and configure the payload to accurately receive and process the signals.

    The LuGRE payload GNSS receiver and low noise amplifier. (Image: NASA/Dave Ryan)
    The LuGRE payload GNSS receiver and low noise amplifier. (Image: NASA/Dave Ryan)

    The Goddard team delivered in February the flight hardware to Firefly Aerospace in Cedar Park, Texas, where it will be integrated into the Blue Ghost lander.

    Astronauts and rovers traversing the lunar surface will need precise location and tracking data for their exploration endeavors. The data gathered from the LuGRE payload will be used to further develop GNSS-based navigation systems for future missions to the moon.

    Image: NASA
    Image: NASA

  • Walmart launches UAV for deliveries in Utah

    Walmart launches UAV for deliveries in Utah

    Image: Walmart
    Image: Walmart

    Two Walmart locations in Utah, one in Lindon and one in Herriman, are now providing UAV delivery for customers nearby. Walmart has UAV deliveries operated by DroneUp, Flytrex and Zipline at 36 stores in the United States.   

    For a $3.99 fee, customers within a mile of the stores can receive their groceries via UAVs. The two Walmart locations in Utah can deliver more than 120 times per day and each UAV can carry up to 10 pounds. The hubs for deliveries are in the parking lots of each Walmart location and are operated by Federal Aviation Administration-certified pilots. 

    Walmart is using UAV delivery in seven states, including Florida, Arizona, Texas, Utah, Virginia, North Carolina and Arkansas. The most common products delivered include ice cream, lemons, rotisserie chicken, Red Bull and paper towels, according to Walmart.   

    Walmart drone deliveries launched in October 2019 in Arkansas. In 2022, Walmart completed more than 6,000 deliveries across all 36 participating locations. 

  • One GPS Mystery Solved, Another Remains

    One GPS Mystery Solved, Another Remains

    Ever since it came on-line in February 2022, the website GPSJam.org has shown what appears to be regular interference with GPS signals in Texas near San Antonio and Del Rio, and locations north and south of Oklahoma City, Oklahoma.

    Only on normal workdays, however. Not on weekends or holidays. Furthermore, whatever was happening also took time off between the Christmas and New Year holidays GPSJam.org also shows similar, though less regular, activity in New Mexico. Experts say this is easily explained as White Sands Missile Range is often the site of electronic warfare training and tests. These are always announced in advance in FAA Notices to Air Missions (NOTAMs) when any interference with GPS reception is anticipated.

    The regular patterns observed in Texas and Oklahoma and the lack of NOTAMs led some experts to speculate the source could be inadvertent interference from a commercial or government activity. Said one former official, “It’s just the kind of pattern you see from large organizations. They are off every weekend, federal holidays, and around Christmas.”

    Aerobatic-capable Military Training aircraft reporting low NIC values (Image: Stanford University)
    Aerobatic-capable Military Training aircraft reporting low NIC values (Image: Stanford University)

    GPSJam.org is the brainchild of aviation analyst John Wiseman. The site uses crowdsourced ADS-B reports gathered by the ADS-B Exchange and displays it on a world map. Areas in yellow indicate that between two and ten percent of ADS-B reports for the day had low navigation accuracy. Areas in red had ten percent or more.

    Information from the site has proved useful in identifying patterns of regular GPS jamming and spoofing in Russia and other conflict areas around the globe.
    The workday patterns in Texas and Oklahoma have appeared on GPSJam.org displays since the site went live in February 2022.

    GPS Interference and Aviation

    Minor interference with GPS signals is fairly common. GPS jamming devices, while illegal to use, are inexpensive and easy to obtain from vendors on the internet.

    Truck drivers wanting to defeat their company’s fleet tracking system, people concerned about being tracked by the government or others, even ministers trying to keep parishioners from texting during sermons – all have been known to use such devices.

    Most GPS interference is unintentional. A two-year European Union study found hundreds of thousands of potentially harmful signals, but judged only about ten percent to be intentional. The rest were the inadvertent byproduct of poorly tuned electrical and electronic equipment.

    ADS-B tracks of training aircraft performing aerobatics. Red indicates low NIC value reported. (Image: Stanford University)
    ADS-B tracks of training aircraft performing aerobatics. Red indicates low NIC value reported. (Image: Stanford University)

    While most GPS interference is unintentional and localized, spurious signals powerful enough to noticeably impact airborne operations are not unknown.

    In two separate incidents last year strong interference near the Denver and Dallas airports impacted air traffic, each for more than a day. The Denver incident lasted for 33 hours before authorities found the source and shut it down. Air traffic was disrupted at Dallas for 44 hours according to government sources, though researchers found the actual interference only lasted for 24 hours. The source of the disruption was never identified.

    In 2019 a passenger aircraft was almost lost due to GPS interference while on approach to Sun Valley, Idaho’s Friedman Memorial Airport. As the aircraft flew a GPS-based approach in smoke and haze, the interfering signal was just strong enough to lure it off course and toward a mountain. Fortunately, a sharp-eyed radar controller hundreds of miles away spotted the problem and intervened in time. The source of the interference was never identified.

    As a result of the Sun Valley incident and input from numerous aviation groups, the International Civil Aviation Organization told its members there was an “urgent need to address harmful interferences” to satnav signals.

    Texas and Oklahoma Mystery Solved

    A researcher at Stanford University finally solved the puzzle of the strange recurring sequence of reports from Texas and Oklahoma.

    While investigating last October’s GPS interference event near the Dallas airport, PhD candidate Zixi Liu noticed aircraft outside the main area of effect also reporting low Navigation Integrity Category (NIC) values. This began before and continued after complaints from commercial airlines about GPS not being available at Dallas-Fort Worth. These aircraft were in the same general area of Texas, but far enough away that there were large areas between them and Dallas that did not contain any reports with low NIC values.

    Low navigation accuracy reports displayed at GPSJam.org. in New Mexico reports were due to GPS interference from military testing. In Texas and Oklahoma, military aerobatics training likely caused reports of low navigation accuracy. (Image: GPSJam.org)
    Low navigation accuracy reports displayed at GPSJam.org. in New Mexico reports were due to GPS interference from military testing. In Texas and Oklahoma, military aerobatics training likely caused reports of low navigation accuracy. (Image: GPSJam.org)

    At the same time MS Liu was also investigating anomalous ADS-B reports near San Antonio and Del Rio, Texas. She discovered in all three cases the reports of low NIC values were coming from military training aircraft regularly used for aerobatics. Other aircraft nearby reported good NIC values and showed no evidence interference.

    In a recent presentation to the Institute of Navigation, she postulated that Interference with GPS signals was not the cause of the low navigation integrity reports. Rather, the rapid maneuvers and unusual aircraft attitudes of aerobatics caused the airplanes’ navigation receivers to intermittently lose lock on signals from GPS satellites. This caused their ADS-B equipment to report low navigation integrity.

    Having solved that mystery, Ms. Liu continues to work on her original question – identifying the source of October’s 24-hour GPS disruption near the Dallas-Fort Worth airport.

    Mr. Dana A. Goward is the President of the Resilient Navigation and Timing Foundation and a former US Coast Guard helicopter pilot.

  • Domino’s delivers with Nuro and GNSS

    Domino’s delivers with Nuro and GNSS

    Photo: Domino's
    Photo: Domino’s

    In April, the pizza company Domino’s and self-driving delivery company Nuro launched autonomous pizza delivery in Houston, Texas. Select customers who place a prepaid online order on certain days and times from Domino’s in Woodland Heights can choose to have their pizza delivered by Nuro’s R2 autonomous, occupantless on-road delivery vehicle.

    Customers selected for the service receive text alerts, which update them on R2’s location and provide them with a unique PIN to retrieve their order. Once R2 arrives, customers are prompted to enter their PIN on a touchscreen, opening its doors.

    In February 2020, Nuro became the first autonomous vehicle developer to be given exemptions by the U.S. National Highway Traffic Safety Administration for testing on public roads without the need to have controls for human operators. Unlike many other autonomous vehicle companies, Nuro engineered its self-driving road vehicles to transport goods instead of people.

    There’s no set timetable for how quickly Domino’s and Nuro will evaluate their testing or expand the service.

    Nuro is also carrying out trials and pilot deliveries with several other companies, including restaurant chain Chipotle, Kroger grocery stores, CVS pharmacies, Walmart and FedEx.