Isolation of resonance in acoustic backscatter from elastic targets using adaptive estimation schemes

The problem of underwater target detection and classification from acoustic backscatter is the central focus of this paper. It has been shown that at certain frequencies the acoustic backscatter from elastic targets exhibits certain resonance behavior which closely relates to the physical properties of the target such as dimension, thickness, and composition. Several techniques in both the time domain and frequency domain have been developed to characterize the resonance phenomena in acoustic backscatter from spherical or cylindrical thin shells. The purpose 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 specular 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 Recursive Least Squares (RLS) learning. The approach taken in this paper is general in the sense that it can be applied to targets of unknown geometry and thickness and, further, does not require any a priori information about the target and/or the environment. Test results on acoustic data are presented which indicate the effectiveness of the proposed approach.