Cambridge Researchers Create Algorithm for Drones to Determine if they Are in the Path of a Plane


On December 18, 2018, suspicious drone sightings near a runway caused London’s Gatwick Airport to shut down until the 21st. As one of the biggest airports in the world, during one of the busiest travel times of the year, nearly 140,000 passengers from 1,000 flights were affected by the disruption. After thorough investigations, no drone or culprit was identified, many believing the incident was caused by mass hysteria. The unconfirmed drone sighting ultimately cost airlines more than $65million and cast a great shadow of doubt over an emerging technology that was poised to help society.

Since the Gatwick incident, negative drone stories have been few and far between. In a short time, we have seen just how beneficial drones have become in fields of search and rescue, agriculture, real estate, entertainment, securities, inspections, emergency services, medical deliveries, and so much more. But that has not stopped supporters of the drone industry from developing ways to ensure drones are not used nefariously. And as more and more drones are being used in shared airspace, this has become even more important to prevent events like the Gatwick Airport incident from ever happening again.

A team of researchers from Cambridge University’s Department of Engineering has come up with a novel algorithm that can determine whether or not a drone’s flight path poses a threat to shared airspace. Led by Professor Simon Godsill, working with senior researchers Dr. Jiaming Liang and Dr. Bashar Ahmad, the team’s algorithm is based on the Bayesian method. As explained in Professor Godsill’s bio, “In the Bayesian approach data is combined with any prior information available in an optimal fashion using probability distributions.” By applying this method to known drone flight behaviors, the team’s system can track drones in real time and predict flight paths. If a drone deviates from these predictions, it can be flagged for further attention before an incident arises.

For commercial and private manned aircraft, Professor Godsill points out, the pilots report their position every few minutes to assist air traffic controllers. “There needs to be some sort of automated equivalent to air traffic control for drones,” said Professor Godsill. “But unlike large and fast-moving targets, like a passenger jet, drones are small, agile, and slow-moving, which makes them difficult to track. They can also easily be mistaken for birds, and vice versa.” For drones to be used in shared airspace there needs to be a way to monitor and identify them.

If a drone is traveling in a way that poses a threat to manned aircraft, air traffic controllers need to be able to react immediately. Actions need to be taken within seconds to protect those on the ground and in the air. At the same time, it is important not to overreact and cause a panic if there is no real threat. Using a Bayesian prediction algorithm, Professor Godwill’s team was able to accurately predict a drone’s trajectory between waypoints, and future waypoints. “In tests, our system was able to spot potential threats in seconds, but in a real scenario, those seconds or minutes can make the difference between an incident happening or not,” said Dr. Liang. “It could give time to warn incoming flights about the threat so that no one gets hurt.”

While any licensed drone operator knows that they have to keep their drone at a certain elevation and clear of designated areas, there is always a chance that someone may forgo these regulations. Because of this, safety redundancy from drone monitoring systems is needed. Professor Godsill said that the monitoring system he and his team designed can be easily and cost efficiently incorporated into existing surveillance models. “While we don’t fully know what happened at Gatwick,” Dr. Ahmad said, “the incident highlighted the potential risk drones can pose to the public if they are misused.” With the team’s monitoring system, incidents like the Gatwick shut down can be prevented.


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