Preprocessing GPR signatures for cascading neural network classification

A solution to the problem of long training times is found by seg?menting classification and param?eter estimation problems into several smaller problems. Information cascades from one neural network to another with each level increasing the specificity of the problem. To be used most effectively, the networks require small input pattern vectors. Therefore much pre-processing is done to extract information from the GPR records that is germane to the classification. A novel method of extracting and enhancing the target reflection through the use of logical filters is developed. A cascading network is constructed that classifies the type of target as point or plane and then iden?tifies the composition. Finally, the size and location of the target are estimated. The modular nature of the network allows it to train faster, give more accurate results and be easily modified.