Presence of a drone and estimating its range simply from the drone audio emissions

Kaliappan Gopalan – kgopala@pnw.edu

Purdue University Northwest, Hammond, IN, 46323, United States

Brett Y. Smolenski, North Point Defense, Rome, NY, USA
Darren Haddad, Information Exploitation Branch, Air Force Research Laboratory, Rome, NY, USA

Popular version of 1ASP8-Detection and Classification of Drones using Fourier-Bessel Series Representation of Acoustic Emissions, presented at the 183rd ASA Meeting.

With the proliferation of drones – from medical supply and hobbyist to surveillance, fire detection and illegal drug delivery, to name a few – of various sizes and capabilities flying day or night, it is imperative to detect their presence and estimate their range for security, safety and privacy reasons.

Our paper describes a technique for detecting the presence of a drone, as opposed to environmental noise such as from birds and moving vehicles, simply from the audio emissions of the drone from its motors, propellers and mechanical vibrations. By applying a feature extraction technique that separates a drone’s distinct audio spectrum from that of atmospheric noise, and employing machine learning algorithms, we were able to identify drones from three different classes flying outdoors with correct class in over 78 % of cases. Additionally, we estimated the range of a drone from the observation point correctly to within ±50 cm in over 85 % of cases.

We evaluated unique features characterizing each type of drone using a mathematical technique known as the Fourier-Bessel series expansion. Using these features which not only differentiated the drone class but also differentiated the drone range, we applied machine learning algorithms to train a deep learning network with ground truth values of drone type, or its range as a discrete variable at intervals of 50 cm. When the trained learning network was tested with new, unused features, we obtained the correct type of drone – with a nonzero range – and a range class that was within the appropriate class, that is, within ±50 cm of the actual range.

Any point along the main diagonal line indicates correct range class, that is, within ±50 cm of actual range, while off-diagonal values correspond to false classification error.

For identifying more than three types of drones, we tested seven different types of drones, namely, DJI S1000, DJI M600, Phantom 4 Pro, Phantom 4 QP with a quieter set of propellers, Mavic Pro Platinum, Mavic 2 Pro, and Mavic Pro, all tethered in an anechoic chamber in an Air Force laboratory and controlled by an operator to go through a series of propeller maneuvers (idle, left roll, right roll, pitch forward, pitch backward, left yaw, right yaw, half throttle, and full throttle) to fully capture the array of sounds the craft emit. Our trained deep learning network correctly identified the drone type in 84 % of our test cases.  Figure 1 shows the results of range classification for each outdoor drone flying between a line-of-sight range of 0 (no-drone) to 935 m.