Abstract-The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods.

Abstract-The problem of detection and classification of buried dielectric anomalies using a separated aperture microwave sensor and an artificial neural network discriminator was considered. Several methods for training and data representation were developed to study the trainability and generalization capabilities of the networks. The effect of the architectural variation on the network performance was also studied. The principal component method was used to reduce the volume of the data and also the dimension of the weight space. Simulation results on two types of targets were obtained which indicated superior detection and classification performance when compared with the conventional methods.