Multi-model interpolation of range-varying acoustic propagation

This paper develops a model-fitting technique to perform the interpolation and extrapolation of a nonlinear time-varying system. The development is demonstrated on the problem of transmission loss of underwater sound. The technique involves simplified time-varying multiple models, neural networks (NNs), and multiobjective simultaneous perturbation stochastic approximation (MSPSA). The simplified models represent the local phenomena that change in time, the NNs capture the model variations, and MSPSA trains the NN-weights. The localized multi-model technique has shown accuracy and efficiency in the transmission loss interpolation.