Genetic Algorithm-Based Acoustic Array Optimization for Estimating UAV DOA Using Beamforming

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.