A Performance Assessment on Rotor Noise-Informed Active Multidrone Sound Source Tracking Methods

Abstract

This study evaluates and assesses the performance of recent developments in sound source tracking using microphone arrays from multiple drones. Stemming from a baseline study, which triangulates the spatial spectrum calculated from the MUltiple SIgnal Classification (MUSIC) for each drone, otherwise known as Particle Filtering with MUSIC (PAFIM), recent studies extended the method by introducing methods to improve the method’s effectiveness. This includes a method to optimise the placement of the drone while tracking the sound source and methods to reduce the influence of high levels of drone rotor noise in the audio recordings. This study evaluates each of the recently proposed methods under a detailed set of simulation settings that are more challenging and realistic than those from previous studies and progressively evaluates each component of the extensions. Results show that applying the rotor noise reduction method and array placement planning algorithm improves tracking accuracy significantly. However, under more realistic input conditions and representations of the problem setting, these methods struggle to achieve decent performance due to factors not considered in their respective studies. As such, based on the performance assessment results, this study summarises a list of recommendations to resolve these shortcomings, with the prospect of further developments or modifications to PAFIM for improved robustness against more realistic settings.