Authors: N. Suresh Kumar; A. Unnikrishnan; C. Bhattacharya
Addresses: Naval Physical and Oceanographic Laboratory, Kochi-682021, India ' Naval Physical and Oceanographic Laboratory, Kochi-682021, India ' Defence Institute of Advanced Technology (DU), Pune-411025, India
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.
Keywords: autonomous underwater vehicles; AUVs; AUV endurance; towed arrays; compressive sensing; DoA estimation; source localisation; acoustic sources; sparse acoustic vector sensor arrays; sensory signal processing; Monte Carlo simulation; direction of arrival.
International Journal of Machine Intelligence and Sensory Signal Processing, 2014 Vol.1 No.2, pp.111 - 131
Available online: 03 Nov 2014 *Full-text access for editors Access for subscribers Purchase this article Comment on this article