The development of unmanned aerial vehicles (UAVs) opens up a lot of opportunities but also brings some threats. Dealing with these threats is not easy and requires some good techniques. Knowing the location of the threat is essential to deal with an UAV that is displaying disturbing behavior. Many methods exist but can be very limited due to the size of UAVs or due to technological improvements over the years. However, the noise produced by the UAVs is still predominant, so it gives a good opening for the development of acoustic methods. The method presented here takes advantage of a microphone array with a processing based on time domain Delay and Sum Beamforming. In order to obtain a better signal to noise ratio, the UAV’s acoustic signature is taken into account in the processing by using a time-frequency representation of the beamformer’s output. Then, only the content related to this signature is considered to calculate the energy in one direction. This method enables to have a good robustness to noise and to localize an UAV with a poor spectral content or to separate two UAVs with different spectral contents. Simulation results and those of a real flight experiment are reported.

The localization of unmanned aerial vehicles is an important topic due to several threats near sensitive sites. Localization based on their sounds has been a particular point of interest in past studies for many years. It requires the use of a microphone array. The positioning of the various microphones making up an antenna defines the intrinsic directivity of the array. In this study, a genetic algorithm is used to determine the microphone positions that optimize directivity in a focus direction and for a frequency, by favoring the narrowness of the main lobe and the reduction of the secondary lobes. The optimization leads to several antennas with a 3D structure similar to that designed in a previous study. A method estimating the direction of arrival of a drone was also presented in that study making use of its acoustic signature to enhance the signal-to-noise ratio and thus improving the estimations. In this paper, an improvement to the method is proposed for tracking the drone’s trajectory. Measurements were conducted to compare the drone locations given by the first designed antenna and the one optimized by the genetic algorithm. Performance on the direction of arrival found is characterized in terms of mean error, standard deviation and root mean square error relative to the GPS reference onboard the UAV. An experiment with the optimized antenna has also been conducted with the drone at a great distance to the antenna to characterize the maximal distance for possible estimations of the direction of arrival. Results show that the method used for the direction of arrival estimation can give a mean error below 10° in azimuth and 5° in elevation. The maximum distance between the antenna and the drone for which the method is able to give estimations is between 240 and 340 m.

Les drones ont vu leurs domaines d’'utilisation augmenter ces dernières années. Ils peuvent être utilisés pour la livraison de colis, les prises de vues aériennes, ou encore des inspections diverses. L’utilisation de ces engins peut néanmoins poser des problèmes de sécurité et de vie privée. En effet, des sites sensibles tels que les aéroports, centrales nucléaires, sites militaires, peuvent être la cible d'attaques de drones ou encore de vols accidentels. Il est donc important d'avoir des méthodespour localiser ces engins afin de réagir face aux différentes menaces. Les travaux de cette thèse portent donc sur l'utilisationdu bruit généré par les drones pour les localiser. L'utilisation d'une antenne de microphones permet avec des traitements d'obtenir la direction d'arrivée du drone. L'étude de la signature acoustique des drones montre que le signal émis possède une structure harmonique. L'utilisation d'une représentation temps-fréquence de la sortie de la formation de voies permet de sélectionner un contenu spectral associé au drone afin d'améliorer les performances de localisation. Des simulations numériques et des expérimentations permettent de valider cette approche. Des améliorations sont également apportées sur l'antenne de microphone avec des optimisations par algorithme génétique. Ces améliorations permettent d'obtenir une meilleure directivité et ainsi de favoriser une bonne localisation.