<title>Recent advances in sonar target classification</title>

Abstract

The discrimination between metallic and nonmetallic targets submerged in water or even embedded in the sea bottom is of some concern for present sonar technology. It can be achieved by processing the backscattered sonar echo from monostatic measurements. Information on the target material and inner structure (rather than merely the outer shape) is obtained only if resonances of the target are excited by the sound pulse. The 'Resonance Scattering Theory' (RST) proves an 'acoustical spectrogram' to characterize the target just as optical spectra do with atoms, molecules, etc. This analogy applies, however, to underwater objects of very small dimensions only; with realistic, full size targets typically very few resonances can be identified due to the strong radiation damping which grows with increasing frequency. It causes wide overlap of the individual resonances so that spectral analysis does not yield a reliable decomposition in the presence of an even small amount of random noise. This has been verified by measurements in a fresh water tank and in a lake. Successful signal processing turns out to be possible, however, by combining resonance excitation of the targets with signal processing in a neural network classifier. Results obtained with power spectral or time series input data to the neural net will be presented. Cylindrical and (hemi) spherical steel shells and several stones have been used as targets. The results obtained so far are quite promising so that a reliable classification is likely to be possible even under more severe operating conditions.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.