With the rapid proliferation of unmanned aerial vehicles, understanding the aeroacoustics of drones in operating conditions is essential to mitigate perceived annoyance. Performing these measurements in a large indoor test hall is particularly attractive, as it allows the execution of complex drone maneuvers under controlled atmospheric conditions, with high-precision trajectory tracking provided by motion capture systems. Yet, not being acoustically treated, these facilities present challenging reverberant conditions for acoustic measurements. This research work focuses on investigating a maneuvering quadcopter drone inside an indoor test hall and proposes a methodology based on phased-array techniques to decontaminate the recorded noise from the reverberation effects using a tailored Green's function. The results indicate that the tonal contributions of the noise spectrum are significantly influenced by drone operation and orientation, with distinct changes in the blade pass frequencies linked to the varying speeds of the front and back rotors during different flight phases. By filtering out spurious broadband noise due to sound reflections, the proposed dereverberation methodology facilitates the tracking of these tonal components, which can be more clearly visualized in the noise spectrum. The study eventually highlights the importance of analyzing the drone trajectory when interpreting the corresponding noise radiation.