Nathan Green

A method for the synthesis and auralisation of multirotor unmanned aerial systems (UAS) overflights is presented. The proposed method allows tracking of the spectral features of the recorded sound generated by an aircraft under a static flight operation. Then, synthesised signals for constant speed overflight are generated by applying spectral modifications accounting for the shifts in rotor rotational speeds. The auralisation process includes sound propagation modelling accounting for the conditions of an outdoor environment. Comparisons between recordings and auralised flight operations show that the proposed method can successfully reproduce the audible stimuli of small multirotor aircraft. The accuracy of the auralisations has been assessed based on sound pressure levels and sound quality metrics. The method presented achieved excellent agreement between the auralised signals and recordings for four aircraft varying in size, takeoff mass, and number of rotors. The applied method contributes to the generation of auditory stimuli that capture UAS spectral features in static flyovers and simulate forward flight operations in realistic outdoor flight scenarios. Building on this capability, the presented auralisation method adds to the set of tools available for assessing human responses to UAS noise.

In this work, we developed a three-dimensional volumetric computational reconstruction algorithm based on conventional integral imaging and axially distributed sensing architectures for a generalized 3 degree-of-freedom multi-perspective 3D sensing configuration.

In this undergraduate research, we studied 3D integral imaging computational reconstruction-based object depth detection by using Polynomial and Spline curve fitting functions. Comparison experiments were conducted to analysis the performance of the proposed methods.