Passive Acoustic Monitoring from a Wave-Propelled Unmanned Surface Vehicle
This project aims to develop passive acoustic monitoring techniques with a novel vessel for applications from environmental sciences to military surveillance.
This project aims to investigate and overcome the challenges associated with enabling passive acoustic monitoring techniques from the AutoNaut wave-propelled unmanned surface vehicle.
The use of towed hydrophone arrays deployed from wave-propelled autonomous surface vehicles presents a unique opportunity for long-duration, wide-area passive acoustic underwater surveillance. Example applications include marine mammal monitoring for environmental surveys and mitigation zone enforcement, as well as anti-submarine warfare. These vessels present certain challenges for robust direction-of-arrival estimation using beamforming. The limited propulsion power compared to traditional tow-vessels imposes the constraint of a short array and a shallow tow depth. Moreover, the intermittent nature of propulsion inherent to these platforms exacerbates the problem of uncertainty in the array profile. Uncompensated array curvature causes beamforming errors, which can lead to inaccurate bearing estimates, misdetections, and false alarms. Using modelling and recursive Bayesian inference techniques, the array motion is being investigated and benchmarked with the intention of developing in-situ compensation techniques.
This project will provide an assessment of the capabilities and challenges associated with utilising this cutting edge novel vessel as a platform from which to perform PAM techniques. The preliminary work and results have been presented at multiple international conferences, producing one conference paper and journal papers on the follow-up work currently in progress.
This project has the potential to develop techniques to allow these novel, low-cost vessels to be used for a multitude of passive acoustic applications ranging from environmental sciences through to military surveillance.
This is the PhD project of Alfie Anthony Treloar, supervised by Dr Alan Hunter.