Predictive system could prevent repeat of Gatwick Airport’s 2018 drone incident

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Predictive system could prevent repeat of Gatwick Airport’s 2018 drone incident

University of Cambridge researchers used a combination of statistical techniques and radar data to predict the flight path of a drone, and whether it intends to enter a restricted airspace, for instance around a civilian airport.

Their solution could help prevent a repeat of the Gatwick incident as it can spot any drones before they enter restricted airspace and pre-determine whether they are likely to pose a threat to other aircraft.

In December 2018, two drones flew into the airport’s airspace forcing the closure of its main runway and causing 48 hours of chaos for thousands of passengers. Anti-drone tech was later installed at the airport in a bid to prevent another incident.

The researchers said their system’s predictive capability can enable automated decision-making and significantly reduce the workload on drone surveillance system operators. Real radar data from live drone trials at several locations was used to validate the new approach.

In 2019, the increasing ubiquity of drones forced the UK Government to make registration mandatory for drone owners and operators in the UK with drones weighing over 250g.

“While we don’t fully know what happened at Gatwick, the incident highlighted the potential risk drones can pose to the public if they are misused, whether that’s done maliciously or completely innocently,” said paper co-author Dr Bashar Ahmad, who carried out the research at Cambridge’s Department of Engineering. “It’s crucial for future drone surveillance systems to have predictive capabilities for revealing, as early as possible, a drone with malicious intent or anomalous behaviour.”

To aid with air traffic control and prevent any possible collisions, commercial airplanes report their location every few minutes. However, there is no such requirement for drones.

“There needs to be some sort of automated equivalent to air traffic control for drones,” said Professor Simon Godsill, who led the project. “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.”

There are several potential ways to monitor the space around a civilian airport. A typical drone surveillance solution can use a combination of several sensors, such as radar, radio frequency detectors and cameras, but it’s often expensive and labour-intensive to operate.

Using the newly developed statistical techniques, the researchers built a solution that would only flag those drones which pose a threat and offer a way to prioritise them. Threat is defined as a drone that’s intending to enter restricted airspace or displays an unusual flying pattern. The software-based solution uses a stochastic, or random, model to determine the underlying intent of the drone, which can change dynamically over time. Most drones navigate using waypoints, meaning they travel from one point to the next, and a single journey is made of multiple points.

In tests using real radar data, the Cambridge-developed solution was able to identify drones before they reached their next waypoint. Based on a drone’s velocity, trajectory and other data, it was able to predict the probability of any given drone reaching the next waypoint in real time.

University of Cambridge researchers used a combination of statistical techniques and radar data to predict the flight path of a drone, and whether it intends to enter a restricted airspace, for instance around a civilian airport.

Their solution could help prevent a repeat of the Gatwick incident as it can spot any drones before they enter restricted airspace and pre-determine whether they are likely to pose a threat to other aircraft.

In December 2018, two drones flew into the airport’s airspace forcing the closure of its main runway and causing 48 hours of chaos for thousands of passengers. Anti-drone tech was later installed at the airport in a bid to prevent another incident.

The researchers said their system’s predictive capability can enable automated decision-making and significantly reduce the workload on drone surveillance system operators. Real radar data from live drone trials at several locations was used to validate the new approach.

In 2019, the increasing ubiquity of drones forced the UK Government to make registration mandatory for drone owners and operators in the UK with drones weighing over 250g.

“While we don’t fully know what happened at Gatwick, the incident highlighted the potential risk drones can pose to the public if they are misused, whether that’s done maliciously or completely innocently,” said paper co-author Dr Bashar Ahmad, who carried out the research at Cambridge’s Department of Engineering. “It’s crucial for future drone surveillance systems to have predictive capabilities for revealing, as early as possible, a drone with malicious intent or anomalous behaviour.”

To aid with air traffic control and prevent any possible collisions, commercial airplanes report their location every few minutes. However, there is no such requirement for drones.

“There needs to be some sort of automated equivalent to air traffic control for drones,” said Professor Simon Godsill, who led the project. “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.”

There are several potential ways to monitor the space around a civilian airport. A typical drone surveillance solution can use a combination of several sensors, such as radar, radio frequency detectors and cameras, but it’s often expensive and labour-intensive to operate.

Using the newly developed statistical techniques, the researchers built a solution that would only flag those drones which pose a threat and offer a way to prioritise them. Threat is defined as a drone that’s intending to enter restricted airspace or displays an unusual flying pattern. The software-based solution uses a stochastic, or random, model to determine the underlying intent of the drone, which can change dynamically over time. Most drones navigate using waypoints, meaning they travel from one point to the next, and a single journey is made of multiple points.

In tests using real radar data, the Cambridge-developed solution was able to identify drones before they reached their next waypoint. Based on a drone’s velocity, trajectory and other data, it was able to predict the probability of any given drone reaching the next waypoint in real time.

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https://eandt.theiet.org/content/articles/2021/09/predictive-system-could-prevent-repeat-of-gatwick-airport-s-2018-drone-incident/

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