Abstract
There is a great interest in the generation of plausible drone signals in various applications, e.g. for auralization purposes or the compilation of training data for detection algorithms. Here, a methodology is presented which synthesises realistic immission signals based on laboratory recordings and subsequent signal processing. The transformation of a lab drone signal into a virtual field microphone signal has to consider a constant pitch shift to adjust for the manoeuvre specific rotational speed and the corresponding frequency dependent emission strength correction, a random pitch shift variation to account for turbulence induced rotational speed variations in the field, Doppler frequency shift and time and frequency dependent amplitude adjustments according to the different propagation effects. By evaluation of lab and field measurements, the relevant synthesizer parameters were determined. It was found that for the investigated set of drone types, the vertical radiation characteristics can be successfully described by a generic frequency dependent directivity pattern. The proposed method is applied to different drone models with a total weight between 800 g and 3.4 kg and is discussed with respect to its abilities and limitations comparing both, recordings taken in the lab and the field.
Highlights
Apart from the many new and fascinating possibilities offered by small unmanned aerial vehicles (UAV) – here called drones – there is growing concern with respect to noise annoyance or even damage
The constant and random pitch shift processes modify the full emission signal, assuming that the frequency of each signal component scales with the rotational speed
The synthesis process is composed of an emission signal generation and a propagation filtering step
Summary
The operation of a drone in an inhomogeneous wind field requires a control mechanism that automatically compensates for changes in lifting force. This results in a random variation of the rotational speed of each rotor. In order to identify the magnitude and time scale of these fluctuations, repeated experiments with a DJI Mavic 2 Pro drone, equipped with low-noise propellers, were performed. For the subsequent signal synthesis, the results of these measurements will be transferred analogously to the other drone models
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