An integrated neurocomputing architecture for side-scan sonar target detection

Abstract

An integrated neurocomputing architecture developed for deployable, real-time pattern recognition applications is described. This architecture, called INCA, consists of a fully parallel, analog electronic, feedforward neural network coupled with a conventional microprocessor system. The first generation system, INCA/1, is currently under construction and employs existing analog neural network building block chips, with an off-the-shelf single-board computer. The proof-of-concept application for INCA/1 is the automatic detection of targets in sidescan sonar images. Preliminary simulations of the network, which account for some of the characteristics of the physical electronics, have shown excellent performance on real data without preprocessing.<>