Acoustic charactreization of small drone flight and maneuvers

First-Person-View (FPV) drones have been deployed to deliver explosive payloads in the battlefield. Conventional methods for detection and tracking such as infrared thermography and radar can be limited due to the maneuverability and small size of these drones, Acoustic methods are used for detection and trajectory tracking ; however, the less research has been done the on the acoustic characterization of specific maneuvers and payload estimation. Multi-rotor quadcopters generate specific noises from their propellers and engine motor vibrations. However, these can be complicated due to its unsteady transitional flow characteristics and dependence on local velocity. The tonal noise signature depends on the blade frequency and harmonic generation. Spectral and time-frequency analysis has recently been used to in quantify movements with the six degrees of freedom. We use Least Mean Square (LMS) adaptive filtering to analysis the spectral content along with amplitude tracking for Black Falcon and d DJI Mini 2 SE with two-four channel recordings. The nonlinear and nonstationary signals are investigated using the Empirical Mode Decomposition (EMD). The Teager-Kaiser Energy Operator is used on Intrinsic Mode Functions (IMFs) to characterize the rapid changes to amplitude and frequency for hovering and movements of pitch, roll, and yaw.