This report investigates how accurately drones can be tracked by measuring the noise they emit
over time with a small microphone array; the ReSpeaker Mic Array v2.0. This acoustic localization
is achieved by applying beamforming to select frequency ranges. In literature this localization has
been achieved and accurately so, but the microphone arrays there are generally at least a meter in
diameter with many microphones. The array used in this project is about 5 cm in diameter and
contains only four microphones. It is therefore cheaper in cost and in terms of calculation time.
The first experiment takes place in an anechoic chamber to compare the beamform output with
reference values, specifying the expected elevation and azimuth angles. The output turns out
to be fairly accurate, with an error of a couple of degrees. A second experiment is with indoor
drone flights, where the reference location is only broadly known. The beamform output at high
frequencies appears to be very accurate and the output at low frequencies is less accurate. This
is expected due to the arrays small size which hinders beamforming at low frequencies but it is
unfortunate as drones emit most of their noise at lower frequencies.
The third experiment is with outdoor drone flights in the Unmanned Valley near Katwijk aan Zee. A
500 gram heavy Parrot Bebop 2 drone with three-bladed propellers can be localized up to a distance of 100m and a 907 gram heavy DJI Mavic 2 drone with two-bladed propellers can be localized up to a distance of 80m. Both with an error between GPS and beamform output of less than 10 degrees
if beamforming is applied to a higher frequency range of 2-5kHz. This only applies if the array
is placed at a 45-degree angle with the ground, which improves the beamforming accuracy. The
application of functional beamforming did not improve the results of this experiment.

Trailing-edge noise can be the dominant noise source of diverse industrial applications, including wind turbines. Noise regulations may limit the power production and installation of new wind farms. There is a need to reduce it. In order to achieve so, we first need to predict it, and to do so we need experimental data to support and validate the new methodologies. A new experimental campaign has been carried out to provide a benchmark of trailing-edge noise applicable to wind turbine noise. It involves a broad range of Reynolds numbers and angles of attack, and covers the noise reduction effect of serrations. This Thesis studies the aforementioned novel data set. The trends of the far-field noise with the forcing of the boundary layer, angle
of attack, and Reynolds number have been studied. Furthermore, scaling laws have been applied to compare different test conditions. The collapse quality has been studied, and the differences have been linked to the aerodynamics and possible post-processing effects. The effect of the serrations has also been studied. Additionally, the noise reduction dependence on the aerodynamic loading is discussed. The influence of the microphone locations on the far-field spectra uncertainty is also studied. The Monte-Carlo method has been applied using the Delay-and-Sum and Clean-SC beamformers. The effect of the input uncertainty correlation, wind tunnel velocity, facility, and algorithm are examined.

A short range hybrid electric aircraft capable of carrying 14 passengers from local (grass) airfields tomajor/commercial airports.