By Zain Naboulsi and Phil Wheat
On Monday morning, about 1 million spectators and 30,000 runners participated in the Boston Marathon, two years after the bombing tragedy that impacted so many people’s lives. An article from BostonHerald.com entitled, “Commissioner: ‘No threats out there’ for race”, mentioned that the city had deployed a number of extra security measures, and one of those was the use of about ten drone-detection units, provided by a company called DroneShield™.
Protecting people from drones is a forward-thinking initiative for public security and absolutely the right thing to do. Perhaps the detectors made people feel a bit more protected from a variety of amorphous threats, too. Unfortunately, the reality is that Boston’s drone-detection was a lot less effective than it might have been. Let’s take a look at the situation.
The system DroneShield deployed uses audio detection—very similar to gunshot-detection systems that have been used in a number of large metropolitan areas. These normally provide suitable validation because there are many of these systems around, but detecting a drone is much different and a lot harder. Gunshot detectors are focused specifically on the loud, sharp report of a shot – this is extremely loud and very simple to hear—a single pulse of sound.
Drones, however, are much more complex. First, drones are quiet compared to a gunshot—most of them are visually and audibly noticeable within a few feet, but that drops off quickly as they get further away. Secondly, the noise they make is so variable that each type of drone has a unique signature and the drones themselves change their sound depending on whether they’re hovering, moving, or even if their propeller blades get worn or nicked.
Hearing the drone is just part of the challenge—recognizing it in a noisy environment is almost impossible. Computer programs exist that are adept at matching sounds against audio patterns—this is how YouTube is able to detect unlicensed songs automatically on its site. But in those cases, the audio track is the only sound and is therefore isolated; if there are other sounds mixed in, it becomes much more difficult to make a match.
For example, if you listen to a YouTube video in which someone is in public and a song can be heard mixed in with the regular day-to-day noise, you’ll likely find the song isn’t tagged—its audio is different enough that the pattern the software is looking for doesn’t match. This the same problem that exists when detecting drones in locations with plenty of ambient noise. This weakness is noted by DroneShield’s founder, Brian Hearing, who says, “…[he’s] eager to see how effectively the sensors filter out crowd and other noises.” He’ll be lucky to have heard anything but a clash of noises.
What about once the drone is detected? The DroneShield system also comes with net guns that were given to police officers—the same types that scientists often use to capture birds for tagging. This seems like a great idea – except the range of the nets are generally 50 feet or less. Drones have to essentially be stationary and quite close to the officer to be caught.
Finally, how do cities go about protecting the public from malicious drones, and why do we care? We are detection experts and we take our job very seriously. We know how the various types of drone detectors work and don’t work. We have spent a great deal of time on this and, full disclosure; we sell a product called Drone Detector™.
Ours is a system that leverages multiple methods to detect if a drone is in use and, if so, what information can be determined about it. We use audio, too, but we amplify the detector’s ability by adding radio frequency and GPS location services so we can spot a drone lots further out—roughly 400 meters. Once we find the drone, we can find the operator.
As drones change and evolve, people will need to continually assess detection systems to ensure that they work as effectively as possible. We encourage everyone interested in this space to do competitive evaluations and determine what works most efficaciously in their area and for their needs.