An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies

An artificial neural network based method for grid-free acoustic source localization using multiple input frequencies

ten Oever, Erik (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)

Delft University of Technology

Aerospace Engineering

2022-04-01

In recent years, efforts are focused on developing an acoustic based autonomous detect and avoidance system for UAVs to minimize interference with other air traffic. The purpose of this research is to study the potential of artificial neural networks for fast, grid-free acoustic source localization. A multi-layer perceptron has been trained to localize simulated white noise acoustic point sources using a converted version of the cross spectral matrix. The ANN based method shows similar localization behaviour to different frequencies as conventional beamforming. A new ANN architecture is proposed that uses the converted cross spectral matrices of multiple different frequencies as input to improve the localization accuracy. The multi input model has shown to have a mean absolute error of approximately 0.27[m]. The proposed model has also been applied on real world recording data of an aircraft flyover. The ANN based method has shown to be able to obtain a prediction within approximately 0.05[s], compared to approximately 1000-2000[s] for conventional beamforming. However, the magnitude and inconsistency of the localization error for the recording is higher compared to the simulated white noise source.

http://resolver.tudelft.nl/uuid:a5713055-c4a4-4a6e-8cdc-4c2ac1e4e300

Student theses

master thesis

© 2022 Erik ten Oever