Acoustic AI Drone Detection Technology
Airspeed is a systems technology company based in the United Kingdom. We have developed an acoustic surveillance system called MANTIS, which uses Edge Computing and Convolutional Neural Networks to detect, locate and track drones for Counter-UAS applications.
The system offers wide-area deployment in complex environments, full 360⁰ tracking of multiple targets and ‘24/7’ autonomous operation. It can be used as a standalone system or integrated into layered Counter-UAS systems by connecting to common data interfaces.
This work has been funded through research grants awarded by the UK Ministry of Defence. We currently have UK and US patent applications pending, which cover the use of distributed acoustic sensors for UAV detection and location estimation.
Acoustic Drone Detection
Small drones can be difficult to detect. Radio Frequency (RF) sensors, such as RADAR may struggle due to the small physical size of the drone, and due to the use of RF transparent materials in their construction (e.g. plastics). In order for a RADAR to ‘see’ a small commercial drone requires the use of high frequencies, such as X-Band or higher (i.e. > 7 GHz). However, radio waves at such high frequencies are attenuated by rain, fog and smoke, which limits the effective range of the sensor. Low level objects such as trees and buildings can also generate clutter in the reflected RADAR signal. Under certain conditions, Electro-Optic (EO) sensors, such as cameras, also have performance limitations due to atmospheric conditions and ambient light levels.
The performance limitations in the sensing technology expose some vulnerabilities in existing counter drone systems. An aggressor may choose to exploit these vulnerabilities by flying at very low level, through ‘urban canyons’ or at night. C-UAS systems which use radiated telemetry emissions from the drone or drone pilot to locate the target drone may also soon become obsolete due to greater use of autonomy, allowing the UAS to complete its mission in ‘radio silence’.
Acoustic sensors can be made to work well for drone detection and tracking in situations where RF and EO sensors do not perform. A robust drone detection and tracking system could be built, which combines the capabilities of a RF or EO sensor with an acoustic sensing system, such as MANTIS. Some existing commercial systems have adopted this approach. It is clear, however, that of these existing systems, the best in class are limited to small-scale ‘point defence’ deployments and require a man-in-the-loop to operate. The proposed MANTIS system offers wide-area deployment in complex environments, full 360⁰ tracking of multiple targets and ‘24/7’ autonomous operation. It would therefore offer a step improvement in system performance.