Source localisation using sparse acoustic vector sensor array

Abstract

This paper addresses the problem of passive source localisation using sparse autonomous underwater vehicle towed array. Any reduction in the sensory signal processing hardware complexity without affecting the acoustic performance significantly improves the endurance capability of the unmanned vehicle. In this paper, we propose a novel sparse acoustic vector sensor (SAVS) array architecture to estimate the direction of arrival (DoA) of multiple acoustic sources. Bearing localisation is effectively achieved by customising the sparse reconstruction pursuit algorithms to suit the SAVS array. The localisation performance of the proposed architecture is analysed and compared with the conventional acoustic pressure sensor (APS) and acoustic vector sensor (AVS)-based array architectures. Theoretical formulation and Monte Carlo simulations for sparse towed array operation in deep ocean is presented. We also develop the expression for the Cramer-Rao bound (CRB) of the variance of DoA for localisation of multiple acoustic sources in generalised Gaussian noise for any AVS array configuration. This proposed architecture enhances the endurance of the vehicle by significantly reducing the number of acoustic sensors, signal conditioning hardware, transmission data rate, number of snapshots and software complexity.