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Valentin Baron

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Proceedings Article | 19 September 2019 Presentation + Paper

Drone localization and identification using an acoustic array and supervised learning

Valentin Baron, Simon Bouley, Matthieu Muschinowski, Jérôme Mars, Barbara Nicolas

Proceedings Volume 11169, 111690F (2019) https://doi.org/10.1117/12.2533039

KEYWORDS: Acoustics, Machine learning, Signal detection, Databases, Vector spaces, Feature extraction

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Drones are well-known threats both in military and civil environments. Identifying them accurately and localizing their trajectory is an issue that more and more methods are trying to solve. Several modalities can be used to make it such as radar, optics, radio-frequency communications and acoustics. Nevertheless radar suffers from a lack of reflected signal for small targets, optical techniques can be very difficult to set in natural environments with small targets, and self-flying drones can avoid radio detection. Consequently, this paper deals with the remaining acoustic modality and aims to localize an acoustic source, then to identify it as a drone or a noise using array measurements and a supervised learning method. The acoustic array allows to determine the source direction of arrival and a spatial filtering is performed to improve the signal to noise ratio. A focused signal is then obtained and used for characterizing the source. The performances obtained to identify this source as a drone or not are compared for two different learning models. The first one uses two classes drone and noise with a classic Support Vector Machine model while the second one is based on an One Class Support Vector Machine algorithm where only the drone class is learned. A database is generated with 7001 observations of drone flights and 3818 observations of noise recordings within a controlled environment where signals are played one at a time, given that an observation is a sequence of 0.2 s of signal. Results of localization show an average error concerning the elevation angle bounded to 3.7° whereas identification results on this database give 99.5 % and 95.6 % accuracies for the two classes approach and the one class approach, respectively. It is shown that this high accuracy is reached thanks to the intrinsic separability of the created data obtained by the different features that have been chosen to compute.

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