Estimation of composite roughness model parameters via a backpropagation neural network

The estimation of the environmental parameters of the Rayleigh-Rice approximation of the composite roughness model and the parameters of the Kirchoff approximation for seafloor acoustic backscatter, given backscatter strength and bathymetric data, is investigated. Estimation of these parameters is an inverse problem whose solution gives surface properties from the measurements of the scattered field. The independence of parameters of both approximations is first determined. Noise-free artificial data are generated by the Rayleigh-Rice approximation and the Kirchoff approximation. These artificial data are then used to train a feedforward neural network using the backpropagation learning algorithm. The trained network is used to estimate the independent parameters of the two approximations