Adaptive extraction of resonances in acoustic backscatter from elastic targets

The problem of underwater target detection and classification from acoustic backscatter is the central focus of this paper. A new approach for identifying the presence of resonance in the acoustic backscatter from unknown elastic targets by isolating the resonance part from the specular contribution is developed. The method allows for characterization of both the time history and time-scale of the resonant contribution of the target directly from the acoustic backscatter. 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. This scheme does not require any underlying model assumption about the elastic return and further can be applied to targets of unknown geometry and thickness. The adaptation rule is based upon fast recursive least squares (RLS) learning. Test results on acoustic data are presented which indicate the effectiveness of the proposed approach