Neural network for underwater target detection

The author proposes the use of a neural network for detecting underwater targets in the presence of random noise. The neutral network is trained to analyze fixed time frames of the input signal to detect the presence or absence of the target, during which the network gets adapted to the local environment and learns to identify the features of the targets. A multilayer neural network is trained to correctly classify many example patterns with and without the target signal present. The back propagation learning rule is employed to update the weights on every presentation of input frames. Once the training is complete the network would be able to tell whether the input frame presented to it contains any target signature.<>