Super-resolution phased array sonar imaging

This dissertation applies super-resolution signal processing alogrithms to image reconstruction for a multibeam phased array sonar. A generic data model for phased array sonars is first developed and is used to derive conventional signal processing concepts for image reconstruction which are necessary for the implementation of the super-resolution algorithms. The most well studied super-resolution algorithms in the literature are reviewed and evaluated for their application to sonar imaging with a focus on rubustness to additive noise and model errors and to their computaitonal efficiency. The algorithms are evaluated on borht by simulation and real data from the ABACUS sonar, an example of a phased array sonar. An efficienct sub-imaging algorithm for implementing super-resuolution imaging for the ABAcus is developed and is compared to the conventional image reconstruction algorithm. Super-resolution inter-ferometric imaging is also developed and evaluated. The Minimum Variance filter is found to be the most suitable algorithm for super-resolution imaging for this type of sonar.

Grantor dc:publisher.institution

Department of Electrical Engineering

Year dc:date.issued

2005

Author dc:creator

• Eccles, Etienne Frederick

Advisor dc:contributor.advisor

• Wilkinson, Andrew John

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/11427/5277

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/5277