Abstract
Current research on classification of submerged objects is concerned with using broadband sonar signals to insonify the targets, and applying signal-processing techniques to the backscattered signals. One characteristic of target echoes which may provide classification clues is the so-called resonance scattering response, the characteristics of which depend on target elastic properties. This paper presents algorithms for extracting resonance information based on autoregressive (AR) spectral estimation techniques. The AR-based representation is useful for detecting and accurately localizing resonances in the frequency domain. The extracted resonance frequencies are grouped into identified wave families, and processed in order to characterize the scatterer in terms of elastic and geometrical parameters on the basis of equations derived from resonance scattering theory. The targets considered are water-loaded elastic, cylindrical, thin-walled shells immersed in salt water under free-field conditions. Analysis was performed on data collected at sea at low-intermediate frequencies [ka∈(0,50)] and provided good results. The proposed approach is a first step towards the characterization of more complex targets either lying proud on the seabed or buried in bottom sediments.
Published Version
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