Sonar detection and classification of buried targets using broadband dolphin-like signals

Abstract

Dolphins have been observed foraging for prey that bury themselves into a sandy bottom. The dolphins swim about 1 to 2 m above the bottom and scan in a circular motion or swim immediately off the bottom, scanning from side to side with their beams pointed approximately normal to the bottom. The dolphin has been emulated by using dolphinlike sonar signals (120-kHz peak frequency, 39-kHz bandwidth, 50-μs duration) in order to classify proud and buried targets in real time. The transducer was attached to a bottom-crawling remotely operated vehicle. Target echoes were received via a cable and digitized at 1 MHz. Short-time Fourier transform and the Morlet wavelet were used to obtain time-frequency representations of the echoes. Echoes were processed in a hierarchical neural network system to perform target classifications. Six targets (cast iron pot, stainless steel sphere, glass jar, concrete tile, and coral rock) were placed either on the ocean bottom or buried into the bottom. Echoes were separated into three target categories: (1) cast iron pot; (2) stainless steel sphere; and (3) remaining four objects. The neural network was able to correctly identify 74%, 97%, and 88% of the category 1, 2, and 3 test echoes.