| Summary: | Approved for public release; distribution is unlimited === This thesis investigates the application of backpropagation neural networks as an
alternative to adaptive filtering at the NUWES test ranges. To facilitate the
investigation, a model of the test range is developed. This model accounts for
acoustic transmission losses, the effects of doppler shift, multipath, and finite
propagation time delay. After describing the model, the backpropagation neural
network algorithm and feature selection for the network are explained. Then, two
schemes based on the network's output, signal waveform recovery and binary code
recovery, are applied to the model. Simulation results of the signal waveform
recovery and direct code recovery schemes are presented for several scenarios.
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