Backpropagation Neural Network for Noise Cancellation Applied to the NUWES TestRanges

摘要

This thesis investigates the application of backpropagation neural networks as analternative 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 times 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.