The wide range of unmanned aerial system (UAS) applications has led to a substantial increase in their numbers, giving rise to a whole new area of systems aiming at detecting and/or mitigating their potentially unauthorized activities. The majority of these proposed solutions for countering the aforementioned actions (C-UAS) include radar/RF/EO/IR/acoustic sensors, usually working in coordination. This work introduces a small UAS (sUAS) acoustic detection system based on an array of microphones, easily deployable and with moderate cost. It continuously collects audio data and enables (a) the direction of arrival (DOA) estimation of the most prominent incoming acoustic signal by implementing a straightforward algorithmic process similar to triangulation and (b) identification, i.e., confirmation that the incoming acoustic signal actually emanates from a UAS, by exploiting sound spectrograms using machine-learning (ML) techniques. Extensive outdoor experimental sessions have validated this system’s efficacy for reliable UAS detection at distances exceeding 70 m.

UASs (Unmanned Air Systems) are universally used in many activities, spanning from leisure-commercial to military applications. Accordingly, as the number of UASs operating in the sky increases, so does the need to detect and identify them, in order to ensure their legitimate use. This paper introduces a continuous wave (CW) Doppler radar implementation that can be used to provide early warning for flying-by small UASs. By applying Fast Fourier Transform (FFT) to the returned signal’s Doppler frequency, estimations can be made regarding the presence of aerial bodies inside an Area of Interest (AoI). Achieving reliable detection with a low false alarm rate (FAR) while keeping the size and power demands of the system to minimum was a challenge that was successfully met. The proposed system was extensively tested in outdoor environments; measurement results are presented and parameters such as radar power, antenna gain, and noise are discussed.

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