Sonar recognition of targets embedded in sediment

Abstract

Dolphins have biological sonar abilities that exceed those of any man-made system in an aquatic environment. One problem of particular importance, and for which only limited capabilities exist, is the detection and recognition of targets buried under sediment. This paper reviews dolphin echolocation capabilities and describes a system that uses a dolphin-like signal and biomimetic signal processing mechanisms to emulate the performance of the dolpin on such targets. The system employed a digitized dolphin click with a center frequency of 120 kHz and a 3dB bandwidth of 39 kHz, 50 μs duration. This signal was transmitted through seawater into mud and the echoes reflected from the objects were recorded and digitized. Two spectral estimators were used to extract a time-frequency representation of the echo. One was based on short-time fast Fourier transforms, and the other was based on an autoregressive estimator. The time-frequency representation was then processed by a separate backpropagation neural network for each estimator, designed to derive independent identifications of the targets. These identifications were then combined in a modified probabilistic neural network that used a linear transfer function rather than a binary function for its output. Finally the output of the probabilistic network was processed symbolically by a simple expert system. Three experiments are described in which the system was used to discriminate a small stainless-steel cylinder from cyliners of the same size made of hollow aluminium, foam-filled aluminium, or coral rock embedded in resin. Each of the targets was presented buried in mud at a depth of several centimeters. The system proved highly effectively at recognizing these buried targets.