With increasing UAV operations, tailored measures need to be implemented to integrate the massive employment of these vehicles in day-to-day operations. One of the current issues of UAVs is the noise emitted by these vehicles. The purpose of this research is to offer a doorway towards understanding the behavior of drone noise with changing operational and design parameters.
The radiated noise from four selected drones was experimentally measured under realistic environmental settings in an open field. The study dives through spectral analysis of a series of maneuvers, assesses the suitability of acoustic imaging algorithms for drone noise sources, explores adequate noise metrics, and lastly, identifies influential drone parameters essential for the modeling of drone noise in the future. Spectrogram and power spectral density analyses unravel the frequency range of interest between 200 Hz to 3000 Hz with dominant harmonics of the rotors’ blade passage frequencies. Above this range, the ground reflection hinders the proper identification of noise sources. Further investigation is done with 4 beamforming algorithms. As drone noise may exhibit complex radiation patterns due to interference of multiple rotors, sophisticated beamforming techniques (i.e. Functional (FB), Dipole (DB), and Quadrupole Beamforming (QB)) were implemented which are potentially superior to Conventional Beamforming (CB) in the setting of drone noise. The results of the beamforming algorithms have been verified with synthetic data. Notably, FB was found to perform best on a source if a correction for the expected source type is applied first, however, it is outperformed by other methods if the source type is not known a priori. While all implemented algorithms yielded excellent results on synthetic data, their accuracy greatly decreases when applied to real-life data. This can be attributed to the multitude of external factors and imperfection in vehicle design, as well as the dependency of DB and QB on prior information regarding the pole orientation on the rotors. In conclusion, only CB could be applied reliably. Analysis of CB over snapshots of drones during different maneuvers shows that the rotors prove to be the main sources of noise in hovering and increasing height from Hover. The source identification, however, becomes unreliable with the change in height. To yield a complementary view on the sound characteristics of drones in specific maneuvers, the sound pressure levels and various sound quality metrics have been computed. The metrics reveal the effects of drone parameters (i.e. rotor size, distance to the observer, thrust settings, etc.), thus leading to a selection of possible candidates for a future noise model. This may be useful to assess the human psychoacoustic annoyance of drone noise in an environment where drones become a part of our everyday life.