ISOLATION OF RESONANCE IN ACOUSTIC BACKSCATTER FROM ELASTIC TARGETS USING ADAPTIVE ESTIMATION SCHEMES

The problem of underwater target detection and classification from aco ustic backscatter is the central focus of this paper. It has been show n that at certain frequencies the acoustic backscatter from elastic ta rgets exhibits certain resonance behavior which closely relates to the physical properties of the target such as dimension, thickness, and c omposition, Several techniques in both the time domain and frequency d omain have been developed to characterize the resonance phenomena in a coustic backscatter from spherical or cylindrical thin shells, The pur pose of this paper is to develop an automated approach for identifying the presence of resonance in the acoustic backscatter from an unknown target by isolating the resonance part from the specular contribution . An adaptive transversal filter structure is used to estimate the spe cular part of the backscatter and consequently the error signal would provide an estimate of the resonance part. An important aspect of this scheme lies in, the fact that it does not require an underlying model for the elastic return. The adaptation rule is based upon fast Recurs ive Least Squares (RLS) learning. The approach taken in this paper is general in the sense that it can be applied to targets of unknown geom etry and thickness and, further, does not require any a priori informa tion about the target and/or the environment. Test results on acoustic data are presented which indicate the effectiveness of the proposed a pproach.