This brilliant plan could stop drones. Too bad it’s illegal
Using both SpotterRF and Blacksage technology- they are working together to detect, identify, and defeat drones.
IMAGINE YOU’RE PART of a great swelling crowd, one of 60,000 people who fill up the cauldron of noise and chaos that is a sold-out football stadium. For you and everyone around you, the game is an open-air gathering place, a chance to steam and scream and worry about nothing except the other team’s menacing D. To the security officials responsible for your safety, it is a constant source of worst-case-scenario planning. They install metal detectors; they enlist a kennel’s worth of bomb-sniffing dogs; they plant concrete pillars around the perimeter to keep out cars; they train personnel in the dark art of bag searching; they even obtain a temporary flight restriction from the FAA to keep all aircraft above 3,000 feet for a radius of 3 miles. They spend millions of dollars and thousands of hours to keep you safe, yet they know that none of it can stop a 3-pound off-the-shelf drone from flying in and dropping something on the crowd. Maybe it’s a toxic mist. Maybe it’s a bomb. Whatever it is, you’ll never see it coming, and because there is currently no legal way to bring down a drone with any accuracy or reliability, there’s nothing anyone can do but wait for it.
In the summer of 2015, Ross Lamm and Dave Romero watched just such a scenario unfold from within a skybox at a large university stadium. The head of security for the college, fearful of the damage drones could do, had decided to run a simulation of a drone attack inside his 60,000-capacity football stadium. (The university asked that identifying details be withheld so as not to share its playbook with would-be attackers.) Campus officials launched a DJI quadcopter, a midsize, midpriced drone, and steered it toward the bleachers, pretending to spread nerve gas on the hundred students gathered below. As the drone looped lazily over the crowd, some of them pretended to vomit convulsively, some twitched spasmodically, some staggered like zombies and then collapsed. Emergency personnel rushed in, assessing the pretend damage and carrying pretend victims out to vans equipped as medical stations.
Up in a skybox, Lamm and Romero, cofounders of Black Sage Technologies, monitored the drone-tracking equipment they’ve spent the past few years developing. Almost immediately after the drone lifted off, Lamm and Romero’s radar detected it. Their AI-powered software identified it as a drone (and not, say, a bird), and their tripod-mounted cameras tracked it as it made its way over the crowd. As they heard the ominous buzzing overhead and watched the college kids pretend to die, Romero and Lamm allowed themselves a small measure of satisfaction—Black Sage’s tracking system worked, and in the event of an actual attack it could give authorities a few crucial extra minutes to mobilize. Mostly, though, Romero and Lamm felt alarmed, knowing all they could do was watch. “Holy shit,” Romero remembers thinking. “We can do everything but stop this catastrophic incident from occurring.”
Shaken and stirred, they returned to Black Sage’s headquarters in Boise, Idaho, and spent a year enhancing their system so that it can now not only track drones but also bring them safely to the ground using radio-frequency-jamming technology. There is only one small hitch: Like almost every drone-interdiction technology in development, frequency jammers run afoul of several US laws, most of which were passed when people hadn’t dreamed of owning their own unmanned aircraft. Romero and Lamm’s solution to the mock terror in the stadium—a solution that they have shown can reliably counter the threats drones pose to targets as varied as prisons, airports, and arenas—is illegal here, which leaves the future of Black Sage’s technology, like the future of drones themselves, very much up in the air.
THE TWO INVENTORS met in 2013 through a mutual friend in Boise. Romero, 31, grew up on a 2,200-acre cattle ranch 50 miles south of the city, the prototypical boy-tinkerer making miracles out of scrap metal. He built lots of dune buggies, motorcycles, and other contraptions, most of which worked, one of which burst into flames. He taught himself computer programming on his family’s IBM 386. After graduating from college in 2007, he started a software company called Tsuvo that performed regression analysis—taking large data sets from disparate government agencies, some of which involved thousands of statistics, and distilling them into clean, color-coded graphics that even nonstatisticians could understand. This kind of massive data crunching and predictive analysis, useful to bureaucracies both here and abroad, led him to live for varying amounts of time in Chile, Palau, and finally, Thailand. It also introduced him to the power of machine-learning algorithms, which helped make quick work of even the thorniest data sets.
Where Romero is an adrenaline fiend—ask about the mountain bike perched in his office and he’ll show you a photo of himself on the bike, halfway through a backflip—Lamm, 45, likes nothing more than sailing with his two sons on a quiet lake. He is deliberate and thoughtful, choosing his words carefully, not out of caution but from an engineer’s appreciation of what’s precise and what’s not. While earning a PhD concentrated on machine vision in the late ’90s, he developed an algorithm that enabled a tractor-mounted camera to tell the difference between cotton plants and weeds, allowing farmers to spray herbicide more accurately. In the aftermath of al Qaeda’s attack on the USS Cole in 2000 (an explosive-laden speedboat crashed into the ship, killing 17 sailors), he helped a US Navy and Coast Guard contractor develop a robotic vision system that allowed ships to detect and quickly respond to speedboat attacks. (With your own vessel rocking and an enemy boat closing in fast, it’s surprisingly difficult to track ships on the water.) He also took part in constructing the warning system in Washington, DC, that locks onto commercial airplanes that drift into restricted airspace and beams an unmistakable red-red-green, red-red-green laser signal into the cockpit to alert the plane’s pilots to fly elsewhere. After more than a decade living and working in Napa Valley, California, he relocated to Boise in 2012, in part so his wife could move her winery there.
Lamm and Romero first crossed paths when their mutual friend asked for their help landing a government contract: The state of Idaho wanted to install a new warning system on a highway to prevent cars from crashing into animals after dark. The existing warning system flashed a light whenever a deer or an elk crossed the road, but because the signal would also light up whenever the wind sent leaves and branches tumbling across the pavement—which was often—drivers came to ignore the warning lights altogether. The highway developed one of the highest wildlife crash rates in the state, and when Romero was home from Thailand for a month visiting his family for Christmas, the friend invited him and Lamm to a brainstorming session at a coffee shop. Could some combination of Lamm’s expertise in robotic vision and Romero’s experience with machine learning help solve the highway problem? “After our friend introduced us, he hardly got a word in,” Romero says. “We got into this virtuous cycle of building on each other’s ideas.”
The pair got to work. Near the highway, they set up a Doppler radar (to detect moving objects) along with an infrared camera (for nighttime viewing) and routed the output to Romero, who had returned to Thailand for a few months to finish some work. To train his machine-learning algorithms to distinguish between animals and clutter, he would spend 45 minutes of his lunchtime each day (perfect for nocturnal sightings in Idaho) watching the infrared images and signaling yes or no as to whether they were wildlife. The system accumulated thousands of data points on the moving objects that crossed the camera’s field of view—speed, acceleration, direction—and once that data was correlated with Romero’s yes/no designations, the algorithm learned to recognize what probably was an animal and what probably wasn’t.
“It’s a beautiful algorithm that takes data from radar and enriches it with close probabilities,” Romero says. Rather than respond to a potential threat like a conventional alarm system—a so-called deterministic response, where almost any stimulus sets off a signal—their system would trigger a probabilistic response. They set the alarm to flash if it determined with a 70 percent probability that the moving object was an elk or a deer as opposed to, say, tumbleweed. False alarms plummeted, drivers began to trust the new system, and in the three months that they field-tested it during the winter of 2014, collisions dropped to zero.
Around the time that Romero and Lamm were focusing on preventing accidents on the ground, more and more people started worrying about crashes in the sky. Once the province of military developers, then of rich folks who could afford the technology, drones soared into the mainstream in 2013 when Chinese drone maker DJI introduced the Phantom, the first consumer-priced unmanned aircraft system. It jump-started what Marke Gibson, the FAA’s drone expert and a former Air Force general, calls “the most fundamental change in aviation in our lifetime.” With hundreds of thousands of new aircraft navigating increasingly crowded airspace, Lamm and Romero noticed there were alarmingly few ways to keep track of the errant ones. What’s more, the radar tracking systems that did exist could rarely distinguish between large birds and drones, a problem similar to what they had encountered on the highway in Idaho. Seeing an opportunity to cash in on an emerging market, Romero and Lamm founded Black Sage in July of 2014 to adapt their wildlife-detection system to the new and more urgent problems posed by drones.
The adaptation wasn’t as simple as taking their existing radar and camera equipment and pointing it skyward, though: Romero and Lamm had to write new software to process the ever-changing latitude, longitude, and altitude of an incoming target, all while taking into account the curvature of the Earth. Lamm wrote “slew-to-cue” algorithms so that whenever the radar picked up an incoming object, it would engage the camera, which then would track the object at a near-continuous 30 times per second. Later he and Romero added an infrared camera to detect the differential heat patterns between drones and the surrounding air. They headed to the scrubby hills above Boise to train the software, aiming the camera and radar at drones as well as the birds riding the thermals and the waterfowl in the wetlands below. For the drones and the birds, the system would measure acceleration, speed, heading, cross-section, surface area, whether the object had moving wings or propellers, and hundreds of other factors. “We didn’t have to know what makes these differences” between drones and other flying objects, Romero says. “The AI figured it out.”
By the summer of 2015 they had a system that could reliably detect an incoming drone about half a kilometer away, identify it, and stay locked on it regardless of evasive maneuvers. It was a breakthrough for them and a potential resource for anyone interested in keeping tabs on nearby drones. When the college security official invited Lamm and Romero to demo their system during the simulated nerve gas attack, he saw firsthand how the Black Sage system could track a drone. He also learned there was nothing that anyone could do to stop it.
YOU’D THINK SHOOTING one down would be the easiest way to do it. After all, in 2015 a guy in Kentucky, pissed off that a drone was hovering over his property, grabbed his shotgun and shot the damn thing out of the sky. Simple enough. But it threw him into a thicket of legal trouble that he couldn’t escape for months. Under FAA rules, drones are considered aircraft: It’s just as illegal to shoot at one as it is to shoot at a Piper Cub, if for no other reason than you can’t control where (or on what or whom) a falling drone will land. The government has taken steps to prevent people from doing dumb things with their drones: Last summer the FAA released licensing and registration rules to compel drone buyers to learn how to fly responsibly. Drone manufacturers have taken actions too, integrating no-fly zones into the aircrafts’ GPS systems. Both measures are easy to get around, though, which explains why the FAA receives more than 100 reports per month of drones flying near aircraft—more than triple the rate it was seeing in 2014. No one knows what would happen if a drone got sucked into a jet engine, although computer simulations at Virginia Tech suggest that it would rip apart the engine’s fan blades in less than 0.005 second.
The problem goes well beyond aircraft. The Pentagon, spurred by reports that ISIS is using drones for surveillance and bomb delivery, has requested $20 million for antidrone research. Recently the Federal Bureau of Prisons posted a request for information on how to equip penitentiaries with antidrone systems (the better to stop drones from dropping contraband into prison yards). “Every prison, every airport, every facility with sensitive equipment outdoors, stadiums, amusement parks, racetracks … everybody is now worried about drones,” says James Williams, an aviation specialist at the international law firm Dentons. In short, what used to be a two-dimensional security problem—stopping intruders at ground level—has now become a three-dimensional one, as security breaches can come from above.
With US sales expected to triple over the next three years, drones are democratizing the air to an unprecedented degree, and Black Sage is only one of a handful of companies trying to solve the problem. One of the more promising, if flawed, systems in the works comes from British company OpenWorks Engineering, which has produced a bazooka-like device called SkyWall 100 that physically captures a drone with a net; the system won a recent competition for drone defense in urban areas, but it’s not effective much beyond 100 meters. In Holland, police have experimented with using eagles to attack drones, but they haven’t figured out how to protect the birds’ feet from the spinning blades, and the raptors have to be trained for months. In the fall of 2015, in their own first attempt to counter a drone, Lamm and Romero rigged a couple of ultra-high-powered spotlights to one of their tripods. When a drone approached, radar would detect it, cameras would track it, and with the touch of a button, 12 million candlepower of light would blind the drone and disable its video and espionage capabilities. It worked well at night, but when they demo’d the system for a customer in the Middle East, the desert sun rendered the lights useless against attacking drones.
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