Towards Flights Renewal – More Security is Needed
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As the incident at Gatwick Airport in December 2018 and other airports demonstrate, the capability to locate both the drone and its controller would have been essential in helping security personnel locate the perpetrators. A new airport system provides such solution.
The CLEARSKY Drone Threat Management system was successfully tested at Bristol (UK) Airport. As part of a 3-year commitment Digital Global Systems (DGS) will provide a network for the detection, classification, and location of unauthorized drones and controllers operating in the flight restriction zones.
The project is in collaboration with deployment partners Telent and Airpoint International.
CLEARSKY’s unique approach towards detecting and classifying drone threats delivers detection distances with classification and critical location information provided in a timely fashion. By combining machine learning techniques and wireless signals management, the system provides accuracy and reliability in the detection process, according to uasweekly.com.
Fernando Murias, Chairman and CEO of DGS said the system ”is designed to operate in chaotic and noisy RF environments, including airports and sports stadiums. Market feedback indicates that other counter-drone vendors have trouble detecting in these real-world environments,” he added.
The removal of false positives is a major challenge for threat detection systems, and CLEARSKY copes with it thanks to its deep learning engine.
Detailed site surveys of the target deployment are performed and the solution is adapted for local conditions.
The system integrates with third-party systems through standard JSON messaging. It can be used with long-range cameras, radar, acoustics, and multi-sensor visualization systems to create layered security. At Bristol Airport, this was leveraged to integrate the Airpoint International mobile application. Airpoint International allows security personnel and law enforcement outside of the control room to track the drone and the pilot location on a mobile application.
The system detects more than just drones. Software options are available to support detection of any anomalous signal in the RF environment, including wireless jammers, unauthorized 2-way radios, a streaming video surveillance camera, or unknown/new drone protocols. Its machine learning engine allows the creation of masks of “normal” RF environments for specific periods in time and compares the current environment to the appropriate mask to detect anomalies.
